Applied Science, Faculty of
Biomedical Engineering, School of
DSpace
UBCV
Ramos Macias, Barbara Monserrat
Master of Applied Science - MASc
University of British Columbia
Introduction: Hypertension in pregnancy, characterized by high blood pressure, is one of the three most common causes of maternal death. Photoplethysmography (PPG) is a portable, non-invasive and low-cost technique, which might have the potential to detect hypertension in pregnancy along with measuring three vital signs: oxygen saturation, heart rate, and respiratory rate. Currently, efforts have been made to estimate blood pressure and detect hypertension with the PPG signal. Objectives: The primary objective of this study is to describe the effect of pregnancy and blood pressure on the PPG signal. Methods: We calculated the average pulse per month of gestational age from 5,072 PPG signals of normotensive women to create a visual representation of a normal pregnancy effect on the PPG morphology. We fitted a linear model using the gestational age, blood pressure, and their interaction to predict PPG features. The PPG features were extracted from 11,412 PPG signals of pregnant women. The PPG features were calculated using four algorithms developed to locate the: dicrotic notch, diastolic peak, systolic peak, start of the pulse, and second derivative waves. Results: We observed PPG morphology changes based on the gestational age in normal pregnancy. The association with blood pressure described in the literature for non-pregnant subjects of some PPG features such as heart rate can change in pregnancy based on the gestational age. The obtained results of the four algorithms are: 1) pulse location algorithm with an accuracy of 97.67% and 98.1% for the start of the pulse and systolic peak location, respectively; 2) pulse verification algorithm improved pulse location positive predictivity; 3) equal accuracy between the ʻaʼ and ʻbʼ waves and the pulses location; and 4) reliable dicrotic notch, diastolic peak and ʻeʼ location based on the comparison with a high-performance dicrotic notch algorithm and visual inspection. Conclusion: The normal cardiovascular changes seen during pregnancy consistently affect the PPG signal and can be visually represented. The gestational age has a significant effect on the PPG detection of hypertension in pregnant women.
Insights into the effect of bloodpressure and pregnancy on the PPGsignalbyBa´rbara Monserrat Ramos Mac´ıasB.Sc., The University of Guadalajara, 2015A THESIS SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFMASTER OF APPLIED SCIENCEinThe Faculty of Graduate and Postdoctoral Studies(Biomedical Engineering)THE UNIVERSITY OF BRITISH COLUMBIA(Vancouver)December 2018c© Ba´rbara Monserrat Ramos Mac´ıas 2018The following individuals certify that they have read, and recommend tothe Faculty of Graduate and Postdoctoral Studies for acceptance, the thesisentitled:Insights into the effect of blood pressure and pregnancy on the PPG signalsubmitted by Ba´rbara Monserrat Ramos Mac´ıas in partial fulfillment of therequirements for the degree of Master of Applied Science inBiomedical EngineeringExamining committee:Guy Albert Dumont, Electrical and computer engineeringSupervisorJohn Mark Ansermino, Anesthesiology, pharmacology and therapeuticsCo-SupervisorPhilippe Kruchten, Electrical and computer engineeringChair nomineeiiAbstractIntroduction: Hypertension in pregnancy, characterized by high blood pres-sure, is one of the three most common causes of maternal death. Photo-plethysmography (PPG) is a portable, non-invasive and low-cost technique,which might have the potential to detect hypertension in pregnancy alongwith measuring three vital signs: oxygen saturation, heart rate, and respi-ratory rate. Currently, efforts have been made to estimate blood pressureand detect hypertension with the PPG signal.Objectives: The primary objective of this study is to describe the effectof pregnancy and blood pressure on the PPG signal.Methods: We calculated the average pulse per month of gestational agefrom 5,072 PPG signals of normotensive women to create a visual represen-tation of a normal pregnancy effect on the PPG morphology. We fitted alinear model using the gestational age, blood pressure and their interactionto predict PPG features. The PPG features were extracted from 11,412 PPGsignals of pregnant women. The PPG features were calculated using fouralgorithms developed to locate the: dicrotic notch, diastolic peak, systolicpeak, start of the pulse, and second derivative waves.Results: We observed PPG morphology changes based on the gestationalage in normal pregnancy. The association with blood pressure described inthe literature for non-pregnant subjects of some PPG features such as heartrate can change in pregnancy based on the gestational age. The obtainedresults of the four algorithms are: 1) pulse location algorithm with an accu-racy of 97.6% and 98.1% for the start of the pulse and systolic peak location,respectively; 2) pulse verification algorithm improved pulse location positivepredictivity; 3) equal accuracy between the ‘a’ and ‘b’ waves and the pulseslocation; and 4) reliable dicrotic notch, diastolic peak and ‘e’ location basedon the comparison with a high-performance dicrotic notch algorithm andvisual inspection.Conclusion: The normal cardiovascular changes seen during pregnancyconsistently affect the PPG signal and can be visually represented. Thegestational age has a significant effect on the PPG detection of hypertensionin pregnant women.iiiLay summaryHypertension in pregnancy, characterized by high blood pressure, is oneof the three most common causes of maternal death. Photoplethysmogra-phy (PPG) is a technique that might have the potential to estimate bloodpressure along with three vital signs: oxygen saturation, heart rate andrespiratory rate. The use of the PPG to estimate blood pressure could beadvantageous because is portable, non-invasive, easy to use, and low-cost.Currently, efforts have been made to estimate blood pressure and detecthypertension with the PPG signal in non-pregnant subjects.We performed an exploratory analysis to describe the effect of bloodpressure and pregnancy in the PPG signal. The results of the exploratoryanalysis shown that gestational age has a significant effect on the detectionof hypertension with the PPG signal in pregnant women.ivPrefaceThe area of research of PPG relation with hypertensive disorders of preg-nancy was provided by Dr. Guy Dumont, Dr. Mark Ansermino and Dr.Peter von Dadelszen. The author contributed by performing a literaturereview in the topic and designing a research program which was supervisedby Dr. Guy Dumont. The author suggested as research topic measuring thecombined gestational age and blood pressure dependence of the PPG signal.Ethics approval required for this research study was obtained by the UBCC&W Research Ethics Board (Certificate#H17-01346).The data used for this research study came from the Community-LevelInterventions for Pre-eclampsia (CLIP) trial performed by the Pre-eclampsiaand eclampsia monitoring, prevention & treatment (PRE-EMPT) group,specifically the PPG signals used in this research study were acquired in theCLIP trial in collaboration with LionsGate Technologies who provided thePhone OximeterTM sensors. The author was responsible for the ethics sub-mission, analysis, interpretation of the results, and writing of each sectionof the thesis. Assistance from the research group was provided for the ethicsapplication. Sample selection and statistical analysis recommendations ac-cording to the CLIP trial database content was provided by the PRE-EMPTgroup, mainly by Dr. Beth Payne, Jeffrey Bone, Dr. Marianne Vidler andDr. Peter von Dadelszen.vTable of ContentsAbstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iiiLay summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ivPreface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vTable of Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . viList of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ixList of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xList of Acronyms . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiiAcknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . xivDedication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xvi1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.1 Photoplethysmography (PPG) signal . . . . . . . . . . . . . 11.1.1 PPG components . . . . . . . . . . . . . . . . . . . . 11.1.2 Measurement protocol, reproducibility and artifacts . 41.1.3 Current and future applications . . . . . . . . . . . . 41.1.4 Potential to detect hypertension in pregnancy . . . . 51.2 Hypertension in pregnancy . . . . . . . . . . . . . . . . . . . 51.2.1 Definition and diagnosis . . . . . . . . . . . . . . . . . 61.2.2 Impact . . . . . . . . . . . . . . . . . . . . . . . . . . 81.2.3 Community-Level Interventions for Pre-eclampsia (CLIP)91.3 General objective . . . . . . . . . . . . . . . . . . . . . . . . 91.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132 PPG and clinical data preprocessing . . . . . . . . . . . . . . 15viTable of Contents2.1 PPG preprocessing . . . . . . . . . . . . . . . . . . . . . . . 152.1.1 PPG filtering . . . . . . . . . . . . . . . . . . . . . . . 152.1.2 Pulse segmentation . . . . . . . . . . . . . . . . . . . 162.1.3 Signal quality index (SQI) . . . . . . . . . . . . . . . 222.2 Clinical data preprocessing . . . . . . . . . . . . . . . . . . . 252.2.1 Clinical data selection . . . . . . . . . . . . . . . . . . 252.2.2 Classification by blood pressure range . . . . . . . . . 252.3 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 263 PPG feature extraction . . . . . . . . . . . . . . . . . . . . . . 283.1 Arterial stiffness/blood pressure related features . . . . . . . 283.1.1 a, b and D detection . . . . . . . . . . . . . . . . . . 343.1.2 Dicrotic notch, diastolic peak and e detection . . . . 383.2 Pulse rate variability (PRV) related features . . . . . . . . . 433.2.1 Time domain . . . . . . . . . . . . . . . . . . . . . . . 433.2.2 Frequency domain . . . . . . . . . . . . . . . . . . . . 443.3 Blood oxygen saturation (SpO2) related features . . . . . . . 453.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 464 Blood pressure and gestational age dependent PPG changes 474.1 Average pulse per month of gestational age . . . . . . . . . . 484.2 Gestational age dependent PPG features . . . . . . . . . . . 484.2.1 Heart rate, anacrotic and catacrotic phase times . . . 514.2.2 Pulse rate variability . . . . . . . . . . . . . . . . . . 544.2.3 Acceleration PPG . . . . . . . . . . . . . . . . . . . . 554.2.4 Skewness, spring constant, dicrotic notch and diastolicpeak position . . . . . . . . . . . . . . . . . . . . . . . 584.2.5 Area under the curve . . . . . . . . . . . . . . . . . . 604.2.6 SpO2 related features . . . . . . . . . . . . . . . . . . 614.3 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 615 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 645.1 Objectives, contributions and limitations . . . . . . . . . . . 645.1.1 Pulse segmentation: peaks and valleys location . . . . 645.1.2 Signal quality index (SQI) implementation . . . . . . 655.1.3 PPG features extraction . . . . . . . . . . . . . . . . 665.1.4 Effect of gestational age, blood pressure and the in-teraction between them in the PPG signal. . . . . . . 685.2 Future work . . . . . . . . . . . . . . . . . . . . . . . . . . . 69viiTable of ContentsBibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71AppendixA Pulse verification algorithm code . . . . . . . . . . . . . . . . 80viiiList of Tables2.1 Performance measures of pulse location algorithms . . . . . . 202.2 Confusion matrix between the estimated and LGT SQI . . . 244.1 Pakistan normotensive women PPG signals per month . . . . 504.2 PPG signals per month and blood pressure group . . . . . . . 514.3 PPG time features in pregnancy per blood pressure group . . 524.4 Original PRV features in pregnancy per blood pressure group 544.5 Anew PRV features in pregnancy per blood pressure group . 554.6 d2PPG features in pregnancy per blood pressure group . . . . 574.7 Skewness, spring constant, dicrotic notch and diastolic peakposition in pregnancy per blood pressure group . . . . . . . . 594.8 SpO2 related features in pregnancy per blood pressure group 614.9 AUC features in pregnancy per blood pressure group . . . . . 62ixList of Figures1.1 PPG methods . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 PPG characteristic parameters . . . . . . . . . . . . . . . . . 21.3 PPG frequency components . . . . . . . . . . . . . . . . . . . 31.4 Light spectrum characteristics of HbO2 and Hb . . . . . . . . 61.5 CLIP general visit flow diagram . . . . . . . . . . . . . . . . . 101.6 CLIP first visit flow diagram . . . . . . . . . . . . . . . . . . 111.7 CLIP subsequent visit flow diagram . . . . . . . . . . . . . . 122.1 Pulse segmentation algorithm . . . . . . . . . . . . . . . . . . 162.2 Moving window algorithm . . . . . . . . . . . . . . . . . . . . 182.3 Moving window algorithm visual representation . . . . . . . . 192.4 Performance example of moving window algorithm . . . . . . 202.5 Pulse verification algorithm . . . . . . . . . . . . . . . . . . . 212.6 Performance example of pulse verification algorithm . . . . . 222.7 Offline SQI implementation . . . . . . . . . . . . . . . . . . . 232.8 Estimated and LGT SQI comparison . . . . . . . . . . . . . . 242.9 Visits blood pressure classification ranges . . . . . . . . . . . 263.1 Amplitudes of PPG characteristic parameters . . . . . . . . . 293.2 Times of PPG characteristic parameters . . . . . . . . . . . . 293.3 Area based PPG features . . . . . . . . . . . . . . . . . . . . 323.4 d2PPG characteristic parameters . . . . . . . . . . . . . . . . 333.5 d2PPG with obtained a, b and e location . . . . . . . . . . . 353.6 dPPG with obtained D location . . . . . . . . . . . . . . . . 363.7 a, b and D detection algorithm visual representation . . . . . 373.8 PPG with obtained dicrotic notch, diastolic peak and e location 393.9 Dicrotic notch, diastolic peak and e detection algorithm part 1 413.10 Dicrotic notch, diastolic peak and e detection algorithm part 2 423.11 Pulse rate variability in frequency and time domain . . . . . . 454.1 Average pulse from month 2-8 of gestational age . . . . . . . 494.2 Average pulse from month 8-10 of gestational age . . . . . . . 49xList of Figures4.3 Heart rate, gestational age and blood pressure group . . . . . 524.4 Anacrotic phase time, gestational age and blood pressure group 534.5 RMSSDb, gestational age and blood pressure group . . . . . 564.6 EA ratio, gestational age and blood pressure group . . . . . . 574.7 ASI, gestational age and blood pressure group . . . . . . . . . 60xiList of AcronymsABP - Arterial Blood PressureAI - Augmentation IndexAPM - Average Pulse per MonthAPS - Average Pulse per SignalAPW - Average Pulse WidthASI - Artery Dtiffness IndexA.C. - Alternate CurrentBPV - Blood Pressure VariabilityCCM - Complex Correlation MeasureCD - Cardiovascular DiseaseCLIP - Community-Level Interventions for Pre-eclampsiac-HCP - Community Health Care ProviderDIC - Disseminated Intravascular CoagulationD.C. - Direct CurrentEDD - Estimated Date of DeliveryFIR - Finite Impulse Response filterGA - Gestational AgeHELLP - Hemolysis Elevated Liver enzymes Low Platelet countHF - High Frequency pulse rate variabilityHR - Heart RateHRV - Heart Rate VariabilityIIR - Infinite Impulse Response filterIQR - InterQuartile RangeLED - Light-Emitting DiodeLF - Low Frequency pulse rate variabilityLGT - LionsGate Technologies, inc.LMICs - Low and Middle-Income CountriesMAP - Mean Arterial PressurePIERS - Pre-eclampsia Integrated Estimate of RiSkPOM - PIERS On the MovePP - Peak to PeakPPG - PhotoPlethysmoGraphyxiiList of AcronymsPP50 - number of successive differences between Peak to Peak intervalslarger than 50 mspPP50 - Percentage of successive differences between Peak to Peak intervalslarger than 50 msPRE-EMPT - PRE-eclampsia and Eclampsia Monitoring, Prevention andTreatmentPRV - Pulse Rate VariabilityRMSE - Root Mean Squared ErrorRMSSD - Root Mean Square Successive DifferencesSDAPP - Standard Deviation of the Averages of Peak to Peak intervalsSDPP - Standard Deviation of Peak to Peak timeSDSD - Standard Deviation of Sucessive DifferencesRPCnp - ReProducibility Coefficient Non-ParametricSpO2 - Blood oxygen saturationSQI - Signal Quality IndexTb98 - percentage of Time with a SpO2 Below 98Tb96 - percentage of Time with a SpO2 Below 96Tb94 - percentage of Time with a SpO2 Below 94TIPP - Triangular Interpolation of the histogram of the Peak to Peak inter-valsULF - Ultra Low Frequency pulse rate variabilityVLF - Very Low Frequency pulse rate variabilityxiiiAcknowledgementsI would like to thank the faculty, staff and research mates who supported meand accompanied me throughout my research. Specially to my supervisorDr. Guy Dumont from whom I always received innovative suggestions tokeep moving forward my research accompanied by his unconditional supportboth professionally and personally. I would like to thank to my co-supervisorDr. Mark Ansermino for sharing with me his valuable clinical knowledge andhis unstoppable curiosity which for me meant a high interest and motivationin my research.I want to thank to all the members of the PRE-EMPT research groupfor their continuous feedback and making this research study possible. Par-ticularly to the UBC PRE-EMPT group who were always willing to hear myconcerns and provide feedback based in their particular expertise. Thankyou to all the UBC PRE-EMPT group for this satisfactory learning journeyand particularly would like to thank to:• Dr. Peter von Dadelszen for sharing his research questions and pas-sion about pre-eclampsia for a better future understanding of this syn-drome and actions that can be taken to prevent maternal mortalityand morbidities.• Dr. Beth Payne for her continuous guiding and introducing me to theclinical predictions models, specially the miniPIERS model.• Dr. Marianne Vidler as a research program manager for directing mydoubts to the specific member of the UBC PRE-EMPT group and thefun team building activities.• Sumedha Sharma for introducing me to the CLIP trial protocol andreviewing my thesis drafts.• Jeffrey Bone and Dr. Vivian Ukah for their recommendations regard-ing statistical analysis.• Domena Tu for continuously delivering to me the updated databasesand PPG signals.xivAcknowledgements• Tang Lee for taking the time to understand all my research study andprovide feedback, as well as sharing with me her data filtering decisionsof the CLIP trial.• Maggie Woo for her organization of meetings, the PRE-EMPT annualmeeting and the fun yoga sessions.I would like to thank to all the members of the Digital Health InnovationLab (DHIL) research group for their continuous feedback and support. Spe-cially to Dustin Dunsmuir for introducing me to the mobile app with whichthe PPG signals was taken during the CLIP trial and the signal qualityindex used in it. Also to Erin Cooke for assisting me with the ethics appli-cation submissions. I want to thank Dr. Klaske van Heusden for taking hertime to understand my research study to provide her feedback when needit. And also Dr. Parastoo Dehkordi for her important collaboration on themoving window algorithm design and Karlen’s algorithms implementation.And thank all my research mates from DHIL group for their continuoussupport and caring, particularly to Brett Hannigan, Rosa Hillgruber, Ce-line Legeuh, Niema Ganad Poor, Dr. Roberto Pagano, Dr. Soodeh Ahani,Shayeste Vefaghnematollahi and Mahdi Yousefi.I would like to thank to Rashika Raizada, Sandeep Sadaram, RishabJain, Elizabeth Tintu, Berenice Molina Ortega, Alejandra Donaji HerreraReyes and Rena´e Zepeda Gutie´rrez for helping me to establish myself inVancouver and the unforgettable moments.I would like to thank to my funders CONACYT and MITACS, withoutthem this project would not have been possible.And more importantly, I want to thank my family: Mar´ıa del RefugioMac´ıas Medina, Manfred Ramos Ponce, Manfred Ramos Mac´ıas y AndreaNaomi Ramos Mac´ıas. Thanks to their love, patience and constant supportI was able to finish my research study, I am very thankful of having themin my life. Their support and encouragement in every decision and step Ihave taken for working in what I feel passionate about was critical for meto achieve it. There is no words to describe how much it means to me.xvDedicationQuiero dedicar mi tesis a mi familia, especialmente a mis padres: Mar´ıa delRefugio Mac´ıas Medina y Manfred Ramos Ponce.xviChapter 1Introduction1.1 Photoplethysmography (PPG) signalThe PPG signal measures the volumetric changes in peripheral blood vessels.These blood volume changes are detected by measuring the transmittanceor reflectance of light as shown in Figure 1.1. The transmittance PPG uses alight source opposite to the photodetector in a translucent part of the bodysuch as the ear or the finger, while the reflectance PPG uses a light sourcenext to the photodetector so the anatomical sites that can be used are notas restricted. Despite the differences, the PPG signals acquired with bothmethods are similar [65].1.1.1 PPG componentsTime domain componentsThe PPG waveform morphology is a consequence of the cardiac cycle andvascular impedances. Both phases of the cardiac cycle can be distinguished:1. The systolic phase, in which the aortic valve of the heart opens andejects oxygenated blood to reach the rest of the body is characterizedin the PPG waveform by the systolic peak as shown in Figure 1.2,(a) Reflectance PPG (b) Transmitance PPGFigure 1.1: PPG methods. In each method a photodetector and typically 2 lightsources with wavelengths of 660 and 940 nm are used (e.g. photodiode and 2 LEDs).The resulting 2 PPG signals are used for the estimation of blood oxygen saturation(SpO2) as explained in subsection 1.1.3. Diagram from Karthik, et al. [33]11.1. Photoplethysmography (PPG) signalFigure 1.2: PPG characteristic parameterswhich corresponds to a major increment in blood volume because ofthe blood ejection.2. The diastolic phase, in which the aortic valve closes while the left ven-tricle is filling and preparing for the next blood ejection is characterizedin the PPG waveform by the diastolic peak and the dicrotic notch asshown in Figure 1.2, where the diastolic peak represents a wave reflec-tion due to differences in impedance across the cardiovascular system.The dicrotic notch is also a consequence of this wave reflection and isrepresented as the valley between the systolic and diastolic peak.As the cardiac cycle, the PPG waveform is divided for its study in twophases: the anacrotic and catacrotic phase. The dividing parameter in thePPG waveform for the anacrotic and catacrotic phase is the systolic peak asshown in Figure 1.2. The anacrotic phase morphology is primarily associatedwith systole while the catacrotic phase morphology varies with the vascularimpedance characteristics and diastole [60].Despite the simple PPG waveform morphology, the origin is not yetfully understood, even when valuable cardiovascular information has beenextracted from it for a wide range of clinical applications.21.1. Photoplethysmography (PPG) signalFigure 1.3: PPG frequency componentsFrequency domain componentsThe PPG waveform is also characterized and interpreted by its frequencycomponents as shown in Figure 1.3:• The baseline (D.C.) component, which is due to constant blood volumeand tissues light absorption.• The pulsatile (A.C.) component which has a frequency band from 0.5–7 Hz, often described as 0.5–4 Hz when only the systolic peak locationis of interest, measures the volume changes related to the cardiac cycleand wave reflections.• The frequency components below 0.5 Hz are usually discarded and— depending on the equipment used for acquiring the PPG signal— may not be present, i.e. if it has an automatic gain controller orauto-centering. The following low frequency components have beendescribed:– A component with a frequency band of 0.2–0.45 Hz, belongs tovolume changes due to respiration.– A low-frequency component of 0.1 Hz called Mayers wave is partof a vascular response to the sympathetic nervous system [8, 32].– A very low-frequency component of 0.01-0.08 Hz is a vascularresponse to the regulation of body temperature [34].31.1. Photoplethysmography (PPG) signal1.1.2 Measurement protocol, reproducibility and artifactsThere is no recognized standard for PPG measurement, which can make itdifficult to compare results from different research studies since the repro-ducibility of the PPG signal can be affected by the site and method of probeattachment, subject, sensor-tissue force, room temperature and acclimati-zation, posture, relaxation, embedded filtering of the device with which thePPG signal was taken, etc [5, 68, 75].Aside from the reproducibility concerns, the PPG signal is vulnerable tospecific artifacts that should be removed during the preprocessing:• Ambient light interference where external light changes are detectedby the photodiode resulting in PPG signal artifacts.• Power interference/electromagnetic noise, 60 or 50 Hz depending onthe country.• Motion artifacts due to finger movement of the patient. This will in-duce different venous pressures that will be detected as changes inblood volume. Motion artifacts can’t be removed with typical tech-niques, such as digital filters because the frequency range of motionartifacts is from 0.1 Hz and above which overlaps with the frequencyrange of the PPG signal. [24, 36].1.1.3 Current and future applicationsThe PPG signal is currently used in commercial medical devices, such asdigital beat-to-beat blood pressure measurement systems, pulse oximeters,which could include respiration and heart rate measurements, and vasculardiagnostics.PPG has potential for further innovation and research has been done ona wide range of topics:• Continuous physiological monitoring: blood oxygen saturation, bloodpressure, respiration rate, heart rate, cardiac output, etc.• Blood vessels characteristics: arterial stiffness, aging, endothelial dys-function, arterial disease, vasospasm, etc.• Autonomic functions: pulse rate variability, thermoregulation, vaso-motor function, orthostatic intolerance, etc. [5].41.2. Hypertension in pregnancyTwo of the most common applications of the PPG signal which are alsoused for this research study are for measuring blood oxygen saturation andheart rate.Blood oxygen saturationBlood oxygen saturation measurement with pulse oximeters has become astandard for monitoring patients in critical care due to its ability to detecthypoxemia. Blood oxygen saturation estimation is done by acquiring twoPPG signals, each with a different light source: one emitting in the redspectrum (660 nm) and the other one in the infrared spectrum (940 nm).The 660 and 940 nm wavelengths are used because of the characteristiclight-absorption difference between deoxyhemoglobin and oxyhemoglobin atthese wavelengths, as shown in Figure 1.4. Finally, for estimating bloodoxygen saturation, pulse oximeter manufacturers calculate a ratio using theAC and DC components of infrared and red PPG signals and transform thisratio to blood oxygen saturation value using a calibrated empirical relation[14, 24].ratio =ACrDCirDCrACirHeart rateHeart rate monitoring is also widely used in critical care due to its ability todetect bradycardia and tachycardia. Heart rate is calculated from the PPGsignal by measuring the time between the systolic peaks [57].1.1.4 Potential to detect hypertension in pregnancyEfforts had been made to estimate the blood pressure in non-pregnant sub-jects [61, 70, 73] and specific PPG features have been associated with arterialstiffness and blood pressure [43, 62, 64]. For this thesis, the blood pressurerelated PPG features are included together with pulse rate variability PPGfeatures due to the possible loss of autonomic function in severe conditionsas seen with complications of hypertension in pregnancy [23, 51].1.2 Hypertension in pregnancyIn this section we describe the definition, diagnosis and impact of hyperten-sion in pregnancy. This section also includes a summary of the community-51.2. Hypertension in pregnancyFigure 1.4: Light spectrum characteristics of oxyhemoglobin and deoxyhemoglobin.Data plotted from [58]level interventions for pre-eclampsia (CLIP), an intervention to improve thecare of hypertension in pregnancy from where the data for this study wascollected.1.2.1 Definition and diagnosisAccording to the American College of Obstetricians and Gynecologists (TheCollege) Task Force on Hypertension on Pregnancy [49], four categories ofhypertension in pregnancy are considered:Pre-eclampsiaPre-eclampsia (PE) is an idiopathic multisystemic disorder that can developduring pregnancy and the postpartum period. The diagnostic criteria in-clude:• Blood pressure– Greater than or equal to 140 mm Hg systolic or greater than orequal to 90 mm Hg diastolic on two occasions at least 4 hours61.2. Hypertension in pregnancyapart after 20 weeks of gestation in a woman with previouslynormal blood pressure.– Greater than or equal to 160 mm Hg systolic or greater thanor equal to 110 mm Hg diastolic, hypertension can be confirmedwithin a short interval (minutes) to facilitate timely antihyper-tensive therapy.And:• Proteinuria– Greater than or equal to 300 mg protein per 24-hour urine col-lection (or this amount extrapolated from a timed collection)– Protein/creatinine ratio greater than or equal to 0.3– Dipstick reading of 1+ (used only if other quantitative methodsare not available)In the absence of proteinuria, new-onset hypertension with the new onsetof any of the following signs or symptoms:• Thrombocytopenia– Platelet count less than 100,000/microliter• Renal insufficiency– Serum creatinine concentration greater than 1.1 mg/dL or a dou-bling of the serum creatinine concentration in the absence of otherrenal diseases• Impaired liver function– Elevated blood concentration of liver transaminases to twice thenormal concentration• Pulmonary edema• Cerebral or visual symptomsChronic hypertensionChronic hypertension is defined as high blood pressure which predates con-ception or was detected before 20 weeks of gestation.71.2. Hypertension in pregnancyChronic hypertension with superimposed pre-eclampsiaChronic hypertension which is complicated by pre-eclampsia is diagnosed inthe following scenarios:• Women with hypertension only in early gestation who develop pro-teinuria after 20 weeks of gestation• Women with proteinuria before 20 weeks of gestation who:– Experience sudden exacerbation of hypertension– Suddenly manifest other signs and symptoms of pre-eclampsia– Sudden, substantial and sustained increases in protein excretionGestational hypertensionGestational hypertension is characterized by new-onset elevation of bloodpressure after 20 weeks of gestation in the absence of accompanying pro-teinuria or the characteristic signs and symptoms of pre-eclampsia.1.2.2 ImpactHypertension in pregnancy is one of the three most common causes of ma-ternal death, from which the majority pertain to low and middle-incomecountries (LMICs) mainly due to a lack of access to proper maternal health-care and awareness [3, 56].Pre-eclampsia can be particularly dangerous because pregnant womenmay not feel sick even when some signs are measurable such as high bloodpressure [2]. When pre-eclampsia is not treated it can progress, increas-ing the risk of a wide range of serious complications: eclampsia (seizures),stroke, coma, retinal/cortical blindness, antepartum/postpartum hemor-rhage, hemolysis elevated liver enzymes low platelet count (HELLP) syn-drome, disseminated intravascular coagulation (DIC), intrauterine growthrestriction, placental abruption/infraction and even maternal/infant death[15]. As a long-term outcome, the preeclamptic woman may have an in-creased risk of developing a cardiovascular disease or stroke later in life [1].In order to prevent maternal mortality and morbidities, research for moreaccessible alternatives to detect pregnant women at an increased risk couldbe an important step forward.81.3. General objective1.2.3 Community-Level Interventions for Pre-eclampsia(CLIP)Significant efforts have been done to improve the care of women with pre-eclampsia and other hypertensive disorders in LMICs, as is the case of thePre-eclampsia and Eclampsia Monitoring, Prevention and Treatment (PRE-EMPT) initiative. CLIP is part of the third objective of the PRE-EMPTproject, consists of three independently powered cluster randomized con-trol trials focused on reducing pre-eclampsia-related and all-cause, maternaland perinatal mortality and major morbidity by 20% or more in interven-tion clusters in Maputo and Gaza Province (Mozambique), Sindh Province(Pakistan) and in Karnataka State (India) [50] (ClinicalTrials.gov Identifier:NCT01911494).As part of the CLIP intervention, the team developed a mobile phone-based tool that rapidly and reliably identifies women in LMICs at increasedrisk of adverse outcomes in the following 48 hours due to pre-eclampsia. Thistool is known as the PIERS on the Move (POM) mobile phone applicationwhich includes: 1) a predictive model based on symptoms and signs calledminiPIERS model and 2) an optional mobile phone adapted pulse oximetrysensor called Phone Oximeter [16].The Phone Oximeter optional addition was only used in Mozambique andPakistan according to the visit flow diagram of figs. 1.5 to 1.7 to increasethe predictive ability by measuring blood oxygen saturation (SpO2) [51] inthe pregnant population under study in the intervention arm.The collected PPG signals provided a volumetric measure of the amountof blood in peripheral vessels, which in addition to oxygen saturation maycapture other hypertension related features, such as vascular compliance,which in a future study could be used to improve the current miniPIERSprediction model. The addition of the pulse oximetry sensor in Mozambiqueand Pakistan allowed collection of 17,043 PPG signals from 15,974 pregnantwomen during the CLIP trials. The PPG signals in combination with clinicalmeasurements recorded during the CLIP visits (blood pressure, gestationalage and antepartum/postpartum status) are used in this research study.1.3 General objectiveThe general objective of the exploratory analysis performed in this thesis isto increase the understanding of the effect of pregnancy and hypertensionon the PPG signal. This information can be used as a criterion to selectthe training sample to develop PPG models which detect hypertension in91.3. General objectiveFigure 1.5: CLIP community Health Care Provider (c-HCP) visit flow diagramgeneral procedure [50]101.3. General objectiveFigure 1.6: CLIP community Health Care Provider (c-HCP) first visit flow diagram[50]111.3. General objectiveFigure 1.7: CLIP community Health Care Provider (c-HCP) subsequent visit flowdiagram [50]121.4. Conclusionpregnancy. The general objective is divided into the following two finalobjectives:• Assess the effect of gestational age on the PPG signal in normotensivewomen.• Assess the effect of gestational age, blood pressure and the interactionbetween them on the PPG signal.To achieve the mentioned final objectives, the PPG signal changes weremeasured by first meeting the following objectives:• Identify pulses in the 14,123 PPG signals which are >35 seconds long.• Implement a signal quality index (SQI) algorithm to discard automat-ically the low-quality sections of the PPG signals.• Develop algorithms to identify the following PPG characteristic pa-rameters: dicrotic notch, diastolic peak, maximum of the first deriva-tive of the PPG and second derivative of the PPG waves (a, b and e).These characteristic PPG parameters will be used to extract the PPGfeatures.• Extract PPG features related to blood pressure, heart rate variabilityand blood oxygen saturation from the PPG signals with an averagehigh-quality SQI.1.4 ConclusionWe focused in this 1st chapter on outlining the hypertension in pregnancyimpact and the PPG signal as a tool that could improve its detection. Indetail this 1st chapter includes:• The PPG signal components and its applications.• The PPG signal potential to detect hypertension in pregnancy.• The hypertension in pregnancy diagnosis and impact.• The CLIP intervention which is focused on the improvement of careof the hypertensive disorders in pregnancy. And the intervention fromwhere the data used for this study was collected.• The thesis objectives.131.4. ConclusionThe general objective of this thesis is a step towards the improvementof hypertension in pregnancy detection through the PPG signal. It is anexploratory analysis to assess if the PPG models to detect hypertension inpregnancy should consider gestational age as a variable or/and as a criterionfor selecting the training sample.The general objective is divided in two final analyses which are describedin the 4th chapter. In the 4th chapter the effect of gestational age and bloodpressure on the PPG features extracted in the 3rd chapter is measured. Be-fore the PPG feature extractions described in the 3rd chapter, the prepro-cessing of the PPG signal and clinical data is described in the 2nd chapter.14Chapter 2PPG and clinical datapreprocessingIn this chapter, the exclusion and inclusion criteria will be explained. Inthe PPG preprocessing section the criteria and methods for discarding ir-relevant/distorted information from the PPG signal are described. A pulsesegmentation algorithm is then described. In the clinical data preprocessingsection, we cover the clinical information selection and classification.2.1 PPG preprocessingThe irrelevant/distorted information from the PPG was discarded following3 main steps: filtering, pulse segmentation, and signal quality index (SQI)estimation.2.1.1 PPG filteringFiltering aims to keep the frequency components of the PPG signal whichare of interest while labeling the rest as noise and minimizing them. Therange of 0.5 to 7 Hz is of interest since it contains the blood volume changesrelated to the cardiac cycle and wave reflections, and we are not interested inextracting respiratory rate from the PPG signal. The specific filters requireddepend on the factors described in Section 1.1.2, as well as on the deviceused to record the signal. Therefore, a specific filter was designed as follows:1. The DC component is eliminated by subtracting the mean.2. Identifying noise distribution and impact: In the frequency domain ofthe PPG signal, the dominant frequencies are lower than 0.5 Hz. Thefrequencies lower than 0.5 Hz are labeled as noise/irrelevant in thisstudy so we aim to minimize them.3. Minimizing noise: An infinite impulse response (IIR) was chosen overa finite impulse response (FIR) filter due to its capability to achieve152.1. PPG preprocessinga similar frequency response with a lower filter order. The filter wasapplied forward and backward to ensure no phase distortions are in-troduced while increasing the frequency response. The selected filterwas a Chebyshev type II order 8 highpass with a cutoff frequency at0.5 Hz and stopband attenuation of 40 dB.4. Verifying absence of electromagnetic noise: Since electricity in Mozam-bique/Pakistan alternates at 50 Hz, this is the frequency of the elec-tromagnetic noise. Considering the signal was sampled at 48 Hz, theperceived frequency of the electromagnetic noise is at 2 Hz due to thealiasing effect. The absence of electromagnetic noise in the signals wasverified because there is no important frequency component at 2 Hz,unless patient heart rate is 120 bpm.5. Selecting a technique to minimize motion artifacts distortion: Somemotion artifact removal algorithms have been developed in which theshape of the waveform could be compromised and complete motionartifacts removal can’t be assured [24, 36, 52]. Therefore, a SignalQuality Index (SQI) is a better approach since it doesn’t modify thePPG waveform and it allows to specifically discard the distorted pulsesfrom each signal.2.1.2 Pulse segmentationFigure 2.1: Pulse segmentation algo-rithmIn order to discard the distortedpulses, the signal must first be seg-mented into pulses. Before the pulsesegmentation, the resolution was im-proved by increasing the signals sam-ple frequency from 48 to 192 Hz by aspline interpolation. Then, pulse seg-mentation was done with a gaussianfilters algorithm [30] described in theblock diagram of Figure 2.1. Averagepulse width estimation, pulse location,and pulse verification are representedby the blue, red and yellow blocks, re-spectively. The inputs are identifiedas boxes with grey color. Each com-ponent of the block diagram will beexplained in detail in this section.162.1. PPG preprocessingAverage pulse width estimationThe average pulse width is estimated by inputing a six second PPG sampleinto a modified gaussian filters algorithm [30]. The modifications are thefollowing:• The input is a “clean” PPG sample selected between three six second-samples. The six second-samples belong to the first 18 seconds of thePPG. The “clean” PPG sample is the one with the maximum cross-correlation between the first and last three seconds.• Two dominant frequencies are chosen as the possible average heartrate after calculating the discrete cosine transform.• The algorithm is not recursive, so only one average pulse width (inverseof average heart rate) and one pulse location is outputed.Pulse locationThe pulse locations are obtained by inputing the PPG signal and the averagepulse width estimation into the moving window algorithm. The movingwindow algorithm detailed in Figure 2.2 consists of the following steps:1. Creating a window with a width equal to the estimated average pulsewidth. As shown in Figure 2.3, the moving window algorithm is toler-ant to a certain degree of estimated average pulse width imprecisenessor pulse rate variability.2. Finding the maximum and minimum in the window. The maximumand minimum location are used to define the first and second interval.3. Finding maximum/minimum in the first interval and saving as peak/pulse.As shown in Figure 2.3b, the use of the first interval avoids erasingprevious real peaks/pulses which can be located in the window.4. Finding maximum/minimum in the second interval and saving as peak/pulse.As shown in Figure 2.3a, the use of the second interval allows erasingmislabelled peaks/pulses and locating the real peak/pulse even whenit is outside the window.5. Advancing the window by half of the window width to complete aniteration.172.1. PPG preprocessingFigure 2.2: Moving window algorithm. Where the input is a PPG signal andthe output is the start of the pulses and systolic peaks location. APW indicatesaverage pulse width; peak, systolic peak location; pulse, start of a pulse location;maxLoc/minLoc, maximum/minimum location in current window or interval.182.1. PPG preprocessing(a) Window width<pulse width (b) Window width>pulse widthFigure 2.3: Moving window algorithm visual representation. Represents two possi-ble scenarios of the moving window algorithm performance which is used to locatethe start of the pulses and systolic peaks on the PPG signal. Where each row repre-sents an iteration. The box indicates the window; black point, maximum/minimumin the window; red circle, peaks/pulses located and black arrow, interval 1/2.192.1. PPG preprocessingFigure 2.4: Systolic peak and start of the pulses location (worst case in the test)obtained with 1) the moving window algorithm and, 2) the gaussian filters algo-rithm.The moving window algorithm was used rather than the Gaussian filtersalgorithm for pulse location since improved performance was achieved asshown in table 2.1 and Figure 2.4. Results shown in table 2.1 and Figure2.4 were obtained from 20 signals within the smaller CLIP identificationnumber of the Pakistan database. For this test, the Gaussian filters andmoving window algorithm were used with a fixed average pulse width.Pulse verificationIn approx. 1 of every 46 PPG signals, at least 10% of the obtained locationswere false positives. If the dicrotic notch is already labeled as the start ofa pulse it will be impossible to locate it as a dicrotic notch. To reduce thefalse positives, the pulse verification algorithm described in Figure 2.5 wasimplemented as shown in Figure 2.6.Pulse location Systolic peak locationMW (%) GF (%) MW (%) GF (%)Accuracy 97.6 93.7 98.1 93.5Sensitivity 98.7 97.3 99.2 96.3Specificity 95.4 86.3 95.7 87.6Table 2.1: Performance measures of the moving window algorithm (MW) and gaus-sian filters algorithm (GF).202.1. PPG preprocessingFigure 2.5: Pulse verification algorithm. This algorithm is used to verify that di-crotic notches and diastolic peaks were not labelled as start of the pulses and systolicpeaks, respectively. The input are start of the pulses and systolic peaks locationand the output are the verified start of the pulses and systolic peaks location.212.1. PPG preprocessingFigure 2.6: Systolic peak and start of the pulse location in one of the worst casescenarios 1)before and 2)after pulse verification algorithmThe pulse verification algorithm compares the pulse amplitude with theprevious and next pulse. In the case that several pulses amplitudes areconsiderably small compared to the neighbour pulses, then these pulses arediscarded. This algorithm is ideally implemented off-line with only a fewlines of code as shown in appendix A.2.1.3 Signal quality index (SQI)After the pulse segmentation, it is possible to discard the low-quality pulsesusing the SQI. The SQI consists of a score per pulse from 0 to 100, where 0means the lowest quality and 100 the higher quality. In this study Karlen’sSQI algorithm [30] was used with the following stages and with modificationsthat are underlined:1. Calibration: Create a pulse reference set by selecting the first 5 pulseswith a correlation>0.95.2. SQI estimation:(a) Measure the correlation between the tested pulse and the longestpulse of the reference set.(b) Estimate SQI from the obtained correlation (C) according toequation 2.1.SQI = 100e400(C+1)99e8(2.1)222.1. PPG preprocessingFigure 2.7: Offline SQI implementation. 1)Filtered PPG signal with a motionartifact section. 2)Backward and forward SQI estimation. 3)SQI merged resultfrom backward and forward SQI estimation(c) Update pulse reference set with the tested pulse if correlation>0.95.3. Offline implementation: SQI estimation was done backward and for-ward, assigning to each pulse the highest SQI obtained as shown inFigure 2.7.To test the performance of the estimated SQI it was compared to thesensor’s provider, LionsGate Technologies (LGT), SQI. The algorithm forLGT SQI is information to which we do not have access but a mean LGTSQI per signal was provided. The comparison was done in the 14,123 signalsthat are >35 seconds long, with the results as follows:• 97% of the signals identified as high-quality by the estimated SQI wereidentified as high-quality by the LGT SQI, as shown in table 2.2.• A confidence interval of the differences between the estimated andLGT SQI of 16• A kurtosis of the differences between the estimated and LGT SQI of8, a higher kurtosis than the one found in a normal distribution.If the LGT SQI per pulse had been provided, then it could have beenused to discard low-quality pulses.232.1. PPG preprocessingEstimated SQIhigh-quality low-qualityLGT SQIhigh-quality 12,496 (97%) 731 (61%)low-quality 427 (3%) 469 (39%)Total 12,923 (100%) 1,200 (100%)Table 2.2: Classification into high-quality (SQI≥70) and low-quality (SQI<70) sig-nals using the estimated and LGT SQI.Figure 2.8: Correlation and Bland-Altman plot between the estimated and LGTSQI. RMSE indicates root mean squared error; r, pearson r-value; IQR, interquartilerange; skewness, test of difference-data; kurtosis, test if difference-data is gaussian;RPCnp, reproducibility coefficient non-parametric estimated as 1.45*IQR; dot lines,represent confidence interval.242.2. Clinical data preprocessing2.2 Clinical data preprocessing2.2.1 Clinical data selectionThe clinical information collection was done during the CLIP visits. TheCLIP visits occurred between the identification of each woman’s pregnancyto 6 weeks postpartum for 4,781 women in Mozambique and 11,399 womenin Pakistan. During each CLIP visit, health workers collected clinical mea-surements as mandated by protocol from which the following were selected:• PPG recording and spO2 signal recorded with the LionsGate Tech-nologies (LGT) pulse oximeter.• Gestational age (days) estimated from the symphysis-fundal height ifthe ultrasound information is unavailable.• Systolic and diastolic blood pressure (mmHg) measured with the Mi-crolife AS1-2 sphygmomanometer.• Antepartum/postpartum status of the visit.For this analysis only antepartum visits were considered. This meansthat an average of 4.4 and 3.5 antepartum CLIP visits per pregnancy inMozambique and Pakistan, respectively were considered. According to theCLIP visit flow diagram of figs. 1.5 to 1.7 the PPG and spO2 signal wererecorded only in 1) first visits and 2) subsequent visits in which the systolicblood pressure is equal or above 140 mmHg.Each PPG recording is a maximum of 1 minute long and contains 2PPG signals: a red (660 nm) and an infrared (940 nm) signal. The redPPG signal was selected for the analysis since the estimated SQI was higherin the red signal compared to the infrared signal implying a higher signalquality. The red PPG signals were included if it were high-quality signals(estimated SQI≥70) and excluded if it were signals of short duration (>35seconds long). A total of 11,412 PPG red signals met the described criteriafrom which 11,083 were collected in first visits and 329 in subsequent visits.2.2.2 Classification by blood pressure rangeTwo different kinds of classification of the clinical data was done: per visitand per women. Clinical data classification by the blood pressure range ofthe visit was done as follows:• Normotensive visit: sbp<120 mmHg and dbp<80 mmHg252.3. ConclusionFigure 2.9: Visits blood pressure classification ranges• Prehypertensive visit: sbp≥120 mmHg or dbp≥80 mmHg and sbp<140mmHg with dbp<90 mmHg.• Hypertensive visit: sbp≥140 mmHg or dbp≥90 mmHgClinical data classification by the blood pressure range of the womenthrough pregnancy was done as follows:• Normotensive women: all the visits were normotensive visits.• Prehypertensive women: at least one prehypertensive visit and with-out any hypertensive visits.• Hypertensive women: at least one hypertensive visit.2.3 ConclusionWe focused this 2nd chapter on describing the PPG and clinical data pre-processing. The PPG preprocessing section includes the methods for:• PPG filtering from noise/irrelevant information.• PPG signal segmentation into pulses, which we performed in 3 stages:average pulse width estimation, pulse location and pulse verification,explaining the algorithms used in each of these stages. Specifically,262.3. Conclusionthe pulses location and pulses verification algorithm were described inthis chapter.• The signal quality index (SQI) estimation. Describing the off-line im-plementation of Karlen’s SQI algorithm [30] used. The obtained high-quality pulses are then used to extract PPG features in the 3rd chapter.The clinical data preprocessing section includes the criteria for:• The selection of the clinical information• The classification by blood pressure range of visits and women.The objective of the PPG preprocessing is filtering the PPG signal andidentifying high-quality pulses that can be used for extracting PPG featuresin the 3rd chapter. Then, the effect of gestational age and blood pressure inthe PPG features extracted in the 3rd chapter is estudied in the 4th chapter.The objective of the clinical data preprocessing is selecting the clinicalinformation that can be used either as inclusion criteria or as variables ofthe two final analyses which are described in the 4th chapter. In the datapreprocessing section we also classified the data as required for the analysesdescribed in this 4th chapter.27Chapter 3PPG feature extractionIn this chapter, the selection and calculation of PPG features is covered inthe following 3 sections:• The first section covers the interpretation and calculation of PPG fea-tures used to assess high arterial stiffness and high blood pressure.Arterial stiffness and blood pressure PPG features are covered in thesame section due to their plausible correlation [27]. As part of thePPG features calculation, algorithms were designed to locate the di-crotic notch, diastolic peak, maximum of the first derivative and secondderivative of the PPG waves (a, b and e).• The second section covers PPG features used to measure pulse ratevariability (PRV), an estimate of heart rate variability (HRV). HRVis a normal homeostatic mechanism, where a reduced HRV is relatedwith a loss of autonomic functions in severe conditions, possibly inhypertensive women at risk of a complication. In this section, the typ-ical PRV features are reviewed. The selection criteria and calculationmethod of the PRV features used are also covered.• The third section covers the blood oxygen saturation features calcu-lation to identify gas exchange impairment, e.g. in a pre-eclampsiacomplication.The PPG features were calculated from the high-quality PPG signals se-lected on the 2nd chapter.3.1 Arterial stiffness/blood pressure relatedfeaturesThe physiological interpretation and calculation of the arterial stiffness/bloodpressure PPG features [11, 18, 41, 43, 60–62, 64, 67, 73, 74] are explainedin this section. An average PPG feature per signal was calculated from thepulses with an SQI≥70.283.1. Arterial stiffness/blood pressure related featuresFigure 3.1: Amplitudes of PPG characteristic parameters: as, systolic peak ampli-tude; an, dicrotic notch amplitude and ad, diastolic peak amplitude.Figure 3.2: Times of PPG characteristic parameters: ts, systolic peak time; tc,catacrotic phase time; tPPI , peak to peak interval time; td, diastolic peak time andtn, dicrotic notch time293.1. Arterial stiffness/blood pressure related features• Augmentation index (AI):– Interpretation: A measure of the augmentation of the systolicpeak by the reflected wave1 (diastolic peak). If the arterial stiff-ness increases, the reflected wave velocity increases arriving ear-lier/closer to the systolic peak. Therefore, the higher the arterialstiffness, the higher the augmentation index.– Calculation: According to equation 3.1, where AI indicates aug-mentation index; ad, diastolic peak amplitude and as, systolicpeak amplitude. Each amplitude is measured from the start ofthe pulse.AI =adas(3.1)• Artery stiffness index (ASI):– Interpretation: The higher the arterial stiffness, the higher theartery stiffness index since the time between the diastolic andsystolic peak decreases.– Calculation: According to equation 3.2, where ASI indicatesartery stiffness index; td, diastolic peak time and ts, systolic peaktime.ASI =1td − ts (3.2)• Dicrotic notch position:– Interpretation: The dicrotic notch is the valley between the di-astolic peak and the systolic peak. So, as the diastolic peak am-plitude increases and is closer to the systolic peak with a higherarterial stiffness, the dicrotic notch amplitude increases and iscloser to the systolic peak.– Calculation: According to equation 3.3, where trn indicates di-crotic notch relative time; tn, dicrotic notch time; ts, systolic peaktime; arn, dicrotic notch relative amplitude; an, dicrotic notchamplitude and as, systolic peak amplitude. Each amplitude ismeasured from the start of the pulse.arn =anastrn = tn − ts (3.3)1Wave reflection origin described on subsection 1.1.1.303.1. Arterial stiffness/blood pressure related features• Anacrotic and catacrotic phase time:– Interpretation: Decreased in subjects with an increased bloodpressure [55, 66].– Calculation: The anacrotic phase time (ts) is the time from thestart of the pulse to the systolic peak and the catacrotic phasetime (tc), the time from the systolic peak to the end of the pulse.• Heart rate:– Interpretation: An increased resting heart rate is associated withcardiovascular disease (CD) [22] and a predictive model of CDhas been developed from heart rate recordings [47]. Also, if theanacrotic and catacrotic phase time decreases with an increasedblood pressure [55], heart rate is expected to increase.– Calculation: According to equation 3.4, whereHR indicates heartrate and tPPI , peak to peak time.HR =60tPPI(3.4)• Skewness:– Interpretation: An increase in the arterial stiffness will cause anearlier arrival of the reflection wave, which can be interpreted asa less skewed shape of the pulse contour.– Calculation: Skewness formula adapted to consider the sampledpulse contour as the probability density function. According toequation 3.5, where the integral symbol indicates the trapezoidalnumerical integration; t, time and a (t), amplitude per time.µ =∫ta (t) dtσ = E(T 2)− µ2E(T 2)=∫t2a (t) dtE(T 3)=∫t3a (t) dtSkewness = E[(X − µσ)3]=E(T 3)− 3µσ2 − µ3σ3(3.5)313.1. Arterial stiffness/blood pressure related featuresFigure 3.3: Area based PPG features• Area under the curve:– Interpretation: Used to analyze pulse morphology variations. Sig-nificant because the time-weighted area under the pulse of thepressure waveform is the mean arterial pressure (MAP) [41]. Andthe pressure waveform morphology is similar to the one of thePPG.– Calculation: The area under the pulse (A), the anacrotic (A1)and the catacrotic (A2) phase were calculated using a trapezoidalnumerical integration. Each pulse was normalized in time andamplitude.• Spring constant:– Interpretation: The higher the arterial stiffness, the lower thespring constant. An estimation of the elasticity of the arterialwall from a model developed by Wei [71].– Calculation: According to equation 3.6, where k indicates springconstant; a (t), amplitude per time and D is the displacementfrom the equilibrium point. D is the volume difference betweenthe location of the maximum point of the first derivative of thePPG and the systolic peak.k = −d2a(t)dt2|at systolic peakD(3.6)323.1. Arterial stiffness/blood pressure related featuresFigure 3.4: PPG and d2PPG with amplitudes of characteristic parameters: aa,amplitude of a; ab, amplitude of b and ae, amplitude of e wave333.1. Arterial stiffness/blood pressure related features• Acceleration photoplethysmography ratios:– Interpretation: The higher the arterial stiffness, the lower the ab-solute value of the BA ratio [28] and EA ratio [69]. The secondderivative of the PPG (d2PPG), also called acceleration PPG, ischaracterized by minimums and maximums in each pulse identi-fied with the letters a, b, c, d and e [64]. In the d2PPG, a reflectsthe acceleration that will lead to the maximum increase in vol-ume; b, the deceleration that together with a shapes the anacroticphase and e, the acceleration that leads to the diastolic peak.– Calculation: According to equation 3.7, where BA indicates BAratio; EA, EA ratio; aa, amplitude of a; ab, amplitude of b andae, amplitude of e. Each amplitude was measured from 0.BA =−abaaEA =aeaa(3.7)In order to calculate this arterial stiffness and blood pressure related fea-tures, the following PPG characteristic parameters are located first: dicroticnotch, diastolic peak, a, b, e and D.3.1.1 a, b and D detectionThe systolic peak and start of the pulse were used to locate a, b and D2 inthe designed algorithm:1. Locate zero of d2PPG.zero = t |d2PPG=0 (3.8)2. Locate D, the maximum in the dPPG between the pulse start andthe systolic peak location.3. Locate a, the maximum in the d2PPG between the last zero beforethe pulse start and D location.4. Locate b, the minimum in the d2PPG between D and the first zeroafter the systolic peak location.2The maximum of the first derivative of the PPG which is used to calculate D, will bereferred as the D wave itself.343.1. Arterial stiffness/blood pressure related featuresFigure 3.5: d2PPG with obtained a, b and e location from a PPG signal in whichthe dicrotic notch is 1) pronounced and 2) unpronouncedThe a, b and D detection algorithm works based on the principle thatlocal maximum and minimum first derivative is zero as follows:1. D is located as the maximum dPPG between the start of the pulse(dPPG zero) and systolic peak (dPPG zero).2. a is located as the maximum d2PPG between the d2PPG zero previ-ous to D and D, which is a d2PPG zero too.3. b is located as the minimum d2PPG between the d2PPG zero laterto D and D, which is a d2PPG zero too.353.1. Arterial stiffness/blood pressure related featuresFigure 3.6: dPPG with obtained D location from a PPG signal in which the dicroticnotch is 1) pronounced and 2) unpronounced363.1. Arterial stiffness/blood pressure related featuresFigure 3.7: a, b and D detection algorithm visual representation. The input are thePPG start of the pulses and systolic peaks location. The output are the maximumof the dPPG (D) and d2PPG waves a and b location. From top to bottom thePPG, dPPG and d2PPG signal. The red circle corresponds to start of the pulse;blue circles, systolic peak; black circle, D; black asterisk, d2PPG zero; red asterisk,a and blue asterisks, b.373.1. Arterial stiffness/blood pressure related features3.1.2 Dicrotic notch, diastolic peak and e detectionThe designed algorithm to detect the dicrotic notch, diastolic peak and, ewave consists of the following steps:1. Define segment (S) to be analyzed: From the systolic peak to the endof the pulse.2. Assure sufficient sampling frequency to locate the zero in d2S. Amore accurate zero location is necessary for this algorithm becauseheights between zero locations will be measured. Spline interpolationis recommended after calculating derivatives and extracting S segmentto increase sample frequency if necessary.3. Locate peaks and valleys of the blood volume flow rate3 (dS), the zerolocation of d2S.zero = t |d2S=0 (3.9)4. Estimate blood volume flow change (C) in each wave.C = d (dS (zeros)) (3.10)5. Identify wave reflection (W), the wave with the maximum blood vol-ume flow change (C). maxLoc indicates the location of the maximum.W = maxLoc (C) (3.11)6. W is relocated if another wave is closer to the mean dicrotic notchheight and has a C value bigger than half of the maximum C.7. Locate acceleration and deceleration section of the wave reflectionzeros = [zeros, length (S)]Acc = zeros (W ) : zeros (W + 1)Dec = zeros (W + 1) : zeros (W + 2) (3.12)8. Locate dicrotic notch, the minimum volume (S) during Acc.9. Locate diastolic peak, the maximum volume (S) during Dec.3The volume flow rate is the volume of fluid that is passing per unit time, which weassume to estimate with the dPPG signal.383.1. Arterial stiffness/blood pressure related featuresFigure 3.8: Obtained dicrotic notch, diastolic peak and e location in a PPG signalwith a dicrotic notch 1) pronounced and 2) unpronounced10. Locate e, the maximum acceleration (d2S) during Acc.The ability of the estimated dicrotic notch to assess changes in bloodpressure and gestational age is tested in the next chapter against 1) theobtained e location and 2) another dicrotic notch location obtained with analgorithm described in [37], this dicrotic notch will be called the delineatordicrotic notch.393.1. Arterial stiffness/blood pressure related featuresThe theory behind the dicrotic notch, diastolic peak and e detectionalgorithm is explained per pulse as follows:1. We assumed that the wave reflection is present after the systolic peakand before the end of the pulse.2. A low sampling frequency of the PPG signal can affect the precisenessof parameters location, so it is recommended to increase the samplingfrequency if necessary.3. The local maximums and minimums of dPPG are located as thed2PPG zeros. The dPPG represents the blood volume flow rate4.4. In each pulse, the maximum blood volume flow increase is due to theheart’s blood ejection during systole. d2PPG zeros are the delimiterschosen to calculate the blood volume flow increase and decrease perdPPG wave.5. In the selected PPG segment, after the systolic peak and before theend of the pulse, we assumed that the maximum blood volume flowincrease is due to the wave reflection. We also assumed that the wavereflection is delimited by its dPPG valleys (the lower blood volumeflow before and after the wave reflection).6. The obtained wave reflection location is verified by comparing it withthe previously wave reflections location in the PPG signal.7. We defined the acceleration section of the wave reflection as the pos-itive d2PPG section and the decceleration section as the negatived2PPG section.8. We assumed the dicrotic notch is the minimum blood volume in theacceleration section.9. We assumed the diastolic peak is the maximum blood volume in thedecceleration section. If the dicrotic notch and diastolic peak are notlocal maximum and minimum, this results in a dicrotic notch anddiastolic peak shared location.10. We assumed the e locations is the maximum d2PPG in the accelerationsection. e is located under this definition even if c and d waves are notapparently found as local maximum and minimum.4The volume flow rate is the volume of fluid that is passing per unit time, which weassume to be estimated with the dPPG signal.403.1. Arterial stiffness/blood pressure related featuresFigure 3.9: Dicrotic notch, diastolic peak and e detection algorithm visual repre-sentation from step 1 to 5. The input are the PPG start of the pulses and systolicpeaks location. The output from the first 5 steps is the location of the maximumdPPG increase after the systolic peak. From top to bottom the PPG, dPPG andd2PPG signal. The transparent black box is used to focus in the PPG segmentfrom the systolic peak to the end of the pulse. The black asterisk indicates d2PPGzero and the red box indicates the maximum blood volume flow increase whichcorresponds to the wave reflection.413.1. Arterial stiffness/blood pressure related featuresFigure 3.10: Dicrotic notch, diastolic peak and e detection algorithm visual rep-resentation from step 6 to 10. The input is the location of the maximum dPPGincrease after the systolic peak. The output from the last 5 steps are the dicroticnotch, diastolic peak and d2PPG wave e location. From top to bottom the PPG,dPPG and d2PPG signal. From top to bottom the PPG, dPPG and d2PPG signal.The transparent black box is used to focus in the PPG segment from the systolicpeak to the end of the pulse. The red arrow represents the acceleration section ofthe wave reflection while the blue arrow the decceleration. The black asterisk in-dicates d2PPG zero; red asterisk, e and blue circle, the diastolic peak and dicroticnotch sharing the same location in this case.423.2. Pulse rate variability (PRV) related features3.2 Pulse rate variability (PRV) related featuresPRV features were analyzed due to its relationship with heart rate variability(HRV) [38, 57]. HRV is a healthy homeostatic mechanism of the humanbody [40, 59] and a decrease of the HRV is correlated with severe conditions[6, 12, 13, 23, 38, 40, 44, 46, 54]. Pre-eclampsia related complications aresevere conditions that might be correlated with a decrease of PRV [20, 46].In this section, PRV features selection and calculation5 is explained:3.2.1 Time domainThe time domain PRV features are classified by its calculation method [10,31, 38] as follows:• Geometric indices, discarded:– PRV triangular index; TIPP, triangular interpolation of the his-togram of the PP intervals; differential index; logarithmic index;etc. Considered robust to artifacts but at least 20 minutes ofPPG signal is used. If obtained PPG signals >20 minutes long,these features were calculated.– Poincare plots: SD1 and SD2. It has been proven that existingPRV features already specify the information provided [9, 26].• Nonlinear methods, discarded:– Fano factor; wavelet variance estimation; allan factor; Fractalmeasures (e.g. power-law correlation, detrended fluctuation anal-ysis, multifractal analysis); entropy measures (e.g. approximateentropy, sample entropy, compression entropy); complex correla-tion measure (CCM); etc. If the physiological interpretation werealready discovered, these features were calculated.• Statistical indices, only SDPP and RMSSD selected:– SDPP, standard deviation of peak to peak (PP) time (tPPI).Represents all components of variability.– RMSSD, the root mean square successive differences. Representssingle pulse variability.RMSSD =√E(dtPPI2)(3.13)5Selected PRV features (SDPP, RMSSD, and HF) were calculated from the tPPI ofthe systolic peak and b peak for comparison in next chapter.433.2. Pulse rate variability (PRV) related features– SDSD, standard deviation of succesive differences. Discarded be-cause if the initial heart rate is not significantly different fromfinal heart rate in the recording, then SDSD ≈ RMSSD.SDSD =√E(dtPPI2)− E (dtPPI)2 (3.14)– SDAPP, standard deviation of the averages of PP intervals in 5-minute segments; SDPP index, mean of the standard deviationsof all PP intervals of 5-minute segments. If obtained PPG signals>15 minute long, these features were calculated.– PP50 and pPP50, number and percentage of succesive differences>50 ms in the entire recording. Highly correlated with RMSSD,RMSSD is preferred.For the time domain PRV features calculation, only pulses with anSQI>50 were analyzed.3.2.2 Frequency domainIn the frequency domain PRV features, the power spectral density of peakto peak time (tPPI) is analyzed. Power spectral density was calculated withthe Lomb-Scargle periodogram, specifically designed for unevenly sampledsignals, as shown in Figure 3.11. The power spectral density of definedfrequency ranges [10, 38] is analyzed for PRV:• ULF, ultralow frequency - 0.0001 to 0.003 Hz• VLF, very low frequency - 0.003 to 0.04 Hz• LF, low frequency - 0.04 to 0.15 Hz• HF, high frequency - 0.15 to 0.4 Hz.• LF normalized, LF/(LF+HF)• HF normalized, HF/(LF+HF)• LF/HF ratioTo analyze each range, the PPG signal length must be at least 10 timesthe wavelength of the lower frequency bound. Therefore, to calculate HF,LF, VLF, and ULF a signal with a length of 1, 4, 55 and 1,666 minutes isrequired, respectively.443.3. Blood oxygen saturation (SpO2) related featuresFigure 3.11: In the top, pulse rate variability (PRV) as peak to peak interval overtime. In the bottom, power of PRV per frequencyWe selected PPG signals with ≥ 57 seconds of continuous SQI>50 con-sidering that 1-minute length should be continuous for HF calculation. Asa final requirement, a minimum heart rate of 48 bpm was set to satisfy theNyquist sampling criterion in the higher frequency bound of HF. From thePPG signals selected according to the criteria described in the 2nd chapter,47.81% met the requirements for HF calculation.3.3 Blood oxygen saturation (SpO2) relatedfeaturesIf the gas exchange is impaired as part of a pre-eclampsia complication, SpO2related features might allow us to identify the gas exchane impairment [51].The following features were calculated from the SpO2 signal:• Mean of SpO2• Standard deviation of SpO2• Delta, the difference between the maximum and minimum SpO2• Tb98, the percentage of time with a SpO2 below 98.• Tb96, the percentage of time with a SpO2 below 96.• Tb94, the percentage of time with a SpO2 below 94.453.4. Conclusion3.4 ConclusionWe focused in this 3rd chapter on describing the selection and calculation ofPPG features. The PPG features are described in 3 sections:• Arterial stifness/blood pressure related PPG features. We explain thecalculation of these PPG features using the location of PPG charac-teristic parameters and include the desgined algorithms to locate thesePPG characteristic parameters: dicrotic nothc, diastolic peak, maxi-mum of the PPG first derivative and PPG second derivative waves (a,b and e).• Pulse rate variability (PRV) related PPG features. We explain thecalculation of these PPG features using the systolic peak locations.We included rationale about why some commonly used PPG featureswere discarded in the analysis.• Blood oxygen saturation (SpO2) related PPG features. We explainthe calculation of these PPG features from the SpO2 signal.The objective of the PPG features calculation is to measure changes inthe PPG signal that are dependent on gestational age and blood pressurein the 4th chapter as part of the two final analyses.46Chapter 4Blood pressure andgestational age dependentPPG changesIn this chapter, the changes of PPG morphology during pregnancy for thestates of normotension, prehypertension and hypertension are analyzed:• In the first section, an average pulse per month of gestation is calcu-lated from normotensive pregnant women. The objective of this visualrepresentation is to facilitate the understanding of how normal cardio-vascular changes during pregnancy could affect the PPG morphology.The obtained visual representation can be used to estimate the di-rection of the changes of unlimited PPG morphology features duringpregnancy. This visual representation clearly shows an evolution of thePPG morphology throughout pregnancy but it provides limited infor-mation of other sources of variation of the PPG morphology. As it isexplained in section 1.1.2, many factors affect the PPG morphology sothe gestational age is only one of them and this should be understoodas a limitation.• In the second section, the effect of gestational age, blood pressurestatus and the interaction between them for each PPG feature is es-timated. The objective is: 1) to assess if the gestational age changesof the PPG differ between a pregnant women who is normotensive,prehypertensive or hypertensive; and 2) to compare the effect of hy-pertension and gestational age. This analysis and its interpretation islimited by the assumption of linear changes and the relatively smallnumber of hypertensive pregnant women in the first trimester.474.1. Average pulse per month of gestational age4.1 Average pulse per month of gestational ageThe maternal cardiovascular system has to evolve during pregnancy sochanges in hemodynamic parameters such as heart rate, stroke volume, andblood pressure have been observed [42]. If heart rate and blood pressurechange, the PPG morphology is expected to change as well [44, 45, 54].However, limited information about the PPG morphology changes throughpregnancy is available [25, 62, 72]. The identification of normal PPG changesduring pregnancy is a step forward to identify abnormal changes and under-stand them.For calculating the average pulse per month (APM) of gestational age,5,072 PPG signals from normotensive subjects were used (see table 4.1).The obtained APM is shown in Figures 4.1 and 4.2. For calculating theAPM we used the following procedure:1. Interpolate 500 samples per pulse with a cubic spline.2. Normalize in time and amplitude each pulse.3. Calculate the average pulse per signal (APS), the average of the pulseswith an SQI≥ 70 of each signal.4. Calculate the average pulse per month (APM), the average of the APSthat belong to each month of gestation.From the obtained APM, PPG morphology changes can be implied. Theobserved changes could be caused by normal changes of the cardiovascularsystem during pregnancy [42] such as the following ones:• The increase of heart rate from the first to third trimester might ex-plain the decrease of the dicrotic notch and diastolic peak amplitude[45] from month 2 to month 8 in Figure 4.1.• The increase of systolic blood pressure and slight decrease of heart ratein term could explain the increase of the dicrotic notch and diastolicpeak amplitude [35, 45] from month 8 to month 10 in Figure 4.2.4.2 Gestational age dependent PPG featuresIn this section, the effect of gestational age, blood pressure status and theinteraction between them for each PPG feature is estimated with a linear re-gression model. The effect estimation is limited by the assumption of linear484.2. Gestational age dependent PPG featuresFigure 4.1: Gestational age dependent PPG morphology changes (Month 2 to 8)Figure 4.2: Gestational age dependent PPG morphology changes (Month 8 to 10)494.2. Gestational age dependent PPG featureschanges and relatively small sample size of hypertensive pregnant women inthe first trimester, which should be considered in the interpretation. Thelinear regression model has the form of equation 4.1, where Y indicatesthe estimated PPG feature value; G, gestational age in months; P , dummyvariable which indicates prehypertension; H, dummy variable which indi-cates hypertension; x0, PPG feature value in the normotensive group at 2months of pregnancy; x1, monthly increment of the PPG feature value inthe normotensive group; x2, increment of PPG feature value at 2 months ofpregnancy if prehypertensive; x3, additional monthly increment of the PPGfeature value if prehypertensive; x4, increment of PPG feature value at 2months of pregnancy if hypertensive and x5, additional monthly incrementof the PPG feature value if hypertensive.Y = x0 + x1G+ x2P + x3GP + x4H + x5GHYnormotensive = x0 + x1GYprehypertensive = x0 + x2 + (x1 + x3)GYhypertensive = x0 + x4 + (x1 + x5)G (4.1)In the linear regression model it was assumed independent observationsbecause in most of the pregnancies only 1 PPG signal was recorded. Also, weproved that the linear mixed effect model results were similar to the resultsof the linear regression model in this case. The sample used to fit the modelsis described in table 4.2. The selected sample is composed of the clinicaldata collected in hypertensive, prehypertensive and normotensive visits6 ofMozambique and Pakistan.The effect of gestational age, blood pressure and the interaction betweenthem in the PPG signal is analyzed through each of the extracted PPGfeatures. Each subsection describes the analysis and interpretation of agroup of PPG features.6Clinical data classification by the blood pressure range of the visit described on section2.2.2Gestational age (Mo.) 2 3 4 5 6 7 8 9 10No. of PPG signals 73 413 726 984 913 801 725 394 43Table 4.1: No. of PPG signals obtained from normotensive women in Pakistan permonth (30 days)504.2. Gestational age dependent PPG featuresMo. Normotensive (9441) Prehypertensive (1545) Hypertensive (426)1 1 0 02 112 29 33 633 105 114 1212 195 185 1706 213 206 1836 271 367 1636 255 648 1313 235 969 796 199 13910 172 41 3811 6 2 1Table 4.2: No. of PPG signals obtained from normotensive, prehypertensive andhypertensive visits in Pakistan and Mozambique combined per month (30 days)4.2.1 Heart rate, anacrotic and catacrotic phase timesThe time-related PPG features of heart rate, anacrotic phase time and cat-acrotic phase time are interpreted together due to their association (wherethe inverse of the heart rate is the average pulse time and the pulse time isthe anacrotic phase time plus the catacrotic phase time).Heart rate results are shown in table 4.3 and Figure 4.3. At month 2of gestation, the hypertension group is associated with an increased heartrate compared to the normotensive. This result is expected because thisrelation has been found between non-pregnant hypertensive and normoten-sive subjects [22, 55]. Surprisingly, the opposite effect is found after month7 of gestation where hypertension is associated with a decreased heart ratecompared to normotension. This effect was also tested in groups: 1st, 2nd,3rd trimester and term with similar trends for each blood pressure group.The decreased heart rate in hypertension is specially interesting since innormotensive pregnant women heart rate usually increases until the thirdtrimester to be able to provide more blood to the growing fetus only decreas-ing slightly at term when there is an increase in stroke volume [42]. I wouldsuggest that after month 7 of gestation, hypertension leads to a decreasedheart rate as a mechanism to try to lower blood pressure.The anacrotic phase time is increased in the hypertension group as com-514.2. Gestational age dependent PPG featuresFigure 4.3: Heart rate, gestational age and blood pressure group. Dashed lineindicates the interquartile range and the solid line, the linear regression model.Table 4.3: Heart rate, anacrotic and catacrotic phase time variations due to gesta-tional age and blood pressure status: regression model resultsNormotensive Prehypertensive Hypertensive(reference) difference differencep rM2 91.48 ± 0.28§ 5.99 ± 0.73§ 7.70 ± 1.8§Heart rate (bpm)MI 0.12 ± 0.07∗ -0.96 ± 0.17§ -1.78 ± 0.34§§ 0.1M2 140 ± 0.7§ 4 ± 1.8† 22 ± 4.6§Anacrotic phasetime (ms)MI -0.5 ± 0.2‡ 0.9 ± 0.4† 3.2 ± 0.9§§ 0.24M2 525 ± 1.9§ -47 ± 4.8§ -69 ± 12.2§Catacrotic phasetime (ms)MI -0.2 ± 0.5 6.7 ± 1.1§ 9.5 ± 2.2§§ 0.12M2 indicates estimate at month 2 of gestation and MI, estimated monthly increment.The model regression coeficients are described as mean ± standard deviation. Normoten-sive visits are used as reference values. Prehypertensive and hypertensive visits are de-scribed with the difference from the normotensive reference value. P-values of regressioncoefficients were calculated from a t-test (two-sided) and p-value of the model was calcu-lated from a F-test. No correction for multiple comparisons was done.§ p < 0.0001, ‡ p < 0.001, † p < 0.01, ∗ p < 0.05524.2. Gestational age dependent PPG featuresFigure 4.4: Anacrotic phase time, gestational age and blood pressure group. Dashedline indicates the interquartile range and the solid line, the linear regression model.pared to normotension, regardless of the gestational age, as shown in table4.3 and Figure 4.4. Surprisingly, the opposite effect was expected since adecrease in the anacrotic phase time has been described as an indicator ofincreased blood pressure in non-pregnant subjects [55, 66]. The anacroticphase time is increased in hypertension even at the gestational age in whichthe heart rate is increased. This result suggests that the effect of hyperten-sion in the finger PPG during pregnancy is different than in non-pregnantsubjects.On the other hand, the catacrotic phase time changes mostly representthe observed heart rate changes as shown in table 4.3. At month 2 of ges-tation, hypertension is associated with a decreased catacrotic phase timecompared to normotension. This it the expected result because this relationhas been found between non-pregnant hypertensive and non-pregnant nor-motensive subjects [55, 66]. However, after month 7 of gestation the relationwith hypertension is shifted to an increased catacrotic phase time comparedto normotension. So, it follows the same patterns as heart rate. Based onthese results I suggest that during pregnancy the catacrotic phase time ismore affected by heart rate while the anacrotic phase time is more affectedby blood pressure.534.2. Gestational age dependent PPG features4.2.2 Pulse rate variabilityPulse rate variability (PRV) was calculated to estimate heart rate variability(HRV). HRV is a measure of the autonomic function and a decrease ofHRV is associated with severe conditions [6, 12, 23, 38, 40, 46]. PRV wasmeasured with: SDPP, which indicates standard deviation of peak to peaktime; RMSSD, root mean square of the successive differences between thepeak to peak times and HF, power spectral density of high frequency (0.15to 0.4 Hz) variability of peak to peak times.PRV features were calculated from 1) the time between systolic peaksas it is originally done and 2) from the time between the b waves of thesecond derivative of the PPG signal. It has been described that HRV canalso be estimated from the second derivative of the PPG [19, 29, 63], so itwas included to compare it with the original PRV in different blood pressurestatus and gestational ages.Table 4.4: Pulse rate variations (systolic peak) due to gestational age and bloodpressure status: regression model resultsNormotensive Prehypertensive Hypertensive(reference) difference differencep rM2 38 ± 0.5§ -3 ± 1.2† -2 ± 3.0SDPP (ms)MI -0.3 ± 0.1‡ 0.7 ± 0.3‡ 0.5 ± 0.5∗ 0.03M2 37 ± 0.6§ -3 ± 1.5 -3 ± 3.8RMSSD (ms)MI -0.7 ± 0.1§ 1.1 ± 0.4 1.3 ± 0.7‡§ 0.05M2 402 ± 23§ -96 ± 68 -173 ± 167HF(ms2)MI -1 ± 6 25 ± 30 14 ± 160.55 0.03M2 indicates estimate at month 2 of gestation and MI, estimated monthly increment.The model regression coeficients are described as mean ± standard deviation. Normoten-sive visits are used as reference values. Prehypertensive and hypertensive visits are de-scribed with the difference from the normotensive reference value. P-values of regressioncoefficients were calculated from a t-test (two-sided) and p-value of the model was calcu-lated from a F-test. No correction for multiple comparisons was done.§ p < 0.0001, ‡ p < 0.001, † p < 0.01, ∗ p < 0.05PRV obtained from the systolic peak does not show any specific trendbetween different blood pressure groups as shown in table 4.4. This re-sult is consistent with other studies which compare the heart rate variabil-ity between hypertension and normotension, in pregnant and non-pregnant544.2. Gestational age dependent PPG featuresTable 4.5: Pulse rate variations (b wave) due to gestational age and blood pressurestatus: regression model resultsNormotensive Prehypertensive Hypertensive(reference) difference differencep rM2 38 ± 0.5‡ -1 ± 1.3 4 ± 3.2SDPPb (ms)MI -0.3 ± 0.1‡ 0.5 ± 0.3 0.9 ± 0.6§ 0.08M2 37 ± 0.6§ -1 ± 1.7 10 ± 4.2†RMSSDb (ms)MI -0.7 ± 0.2§ 0.9 ± 0.4† 2.2 ± 0.8‡§ 0.15M2 423 ± 24§ -75 ± 70 -23 ± 172HFb(ms2)MI -1 ± 6 12 ± 31 14 ± 160.32 0.03M2 indicates estimate at month 2 of gestation and MI, estimated monthly increment.The model regression coeficients are described as mean ± standard deviation. Normoten-sive visits are used as reference values. Prehypertensive and hypertensive visits are de-scribed with the difference from the normotensive reference value. P-values of regressioncoefficients were calculated from a t-test (two-sided) and p-value of the model was calcu-lated from a F-test. No correction for multiple comparisons was done.§ p < 0.0001, ‡ p < 0.001, † p < 0.01, ∗ p < 0.05subjects [21, 39]. However, PRV from the systolic peak in normotensivepregnant women is slightly decreased throughout pregnancy. I would sug-gest that the decrease may be caused by the heart rate increment [44, 54]rather than a decreased functionality of the autonomic system in this case.PRV obtained from the b wave, specifically SDPPb and RMSSDb, isincreased during hypertension compared to normotension as shown in table4.5 and Figure 4.5. This increase has been observed in blood pressure vari-ability (BPV) in pregnant and non-pregnant hypertensive subjects ratherthan in heart rate variability [21, 39]. The existence of a difference in BPVbetween hypertension and normotension is confirmed with the differencesin the autonomic control of blood pressure described between hypertensiveand normotensive pregnant women [7, 17]. Considering the PPG signal isaffected by blood pressure changes, the PRV obtained from the PPG secondderivative might be a measure which includes BPV and HRV effect.4.2.3 Acceleration PPGWe analyzed the effect of blood pressure and gestational age in the BA andEA ratios, b and e wave amplitude divided by a wave amplitude of the PPG554.2. Gestational age dependent PPG featuresFigure 4.5: RMSSD from b, gestational age and blood pressure group. Dashed lineindicates the interquartile range and the solid line, the linear regression model.second derivative, also called acceleration PPG. A decrease on the absolutevalue of EA and BA ratio has been described during hypertension and in-creased arterial stiffness [28, 53, 64, 69], so the results will be interpretedunder this assumption.The absolute value of the BA ratio (|BA|) is decreased in hypertensioncompared to normotension in pregnant women as shown in table 4.6. Thisresult is consistent with the expected relation between hypertension andnormotension. The relation in |BA| between being hypertensive and nor-motensive apparently becomes more evident as gestational age increases,which might be caused by the higher blood pressure values in each group asgestational age increases. As with other PPG features, all the variations inthe BA ratio can not be explained by the gestational age and blood pressurestatus alone.The EA ratio (EA) is increased in hypertensive pregnant women asshown in table 4.6 and Figure 4.6. Surprisingly, the opposite relation wasexpected: a decrease in hypertension compared to normotension. The rea-sons that lead to the opposite effect in EA during pregnancy are unknown.The difference in EA between being hypertensive and normotensive alsobecomes more evident as gestational age increases.564.2. Gestational age dependent PPG featuresFigure 4.6: EA ratio, gestational age and blood pressure group. Dashed line indi-cates the interquartile range and the solid line, the linear regression model.Table 4.6: |BA| and EA ratio variations due to gestational age and blood pressurestatus: regression model resultsNormotensive Prehypertensive Hypertensive(reference) difference differencep rM2 88 ± 0.26§ -1 ± 0.68† -5 ± 1.72‡|BA| ratio (%)MI 0.51 ± 0.06§ -0.04 ± 0.16 -8.8 ± 0.31‡§ 0.17M2 20.9 ± 0.16§ 1.3 ± 0.42‡ 5.0 ± 1.06§EA ratio (%)MI 0.35 ± 0.04 § 0.04 ± 0.10 5.8 ± 0.19‡§ 0.25M2 indicates estimate at month 2 of gestation and MI, estimated monthly increment.The model regression coeficients are described as mean ± standard deviation. Normoten-sive visits are used as reference values. Prehypertensive and hypertensive visits are de-scribed with the difference from the normotensive reference value. P-values of regressioncoefficients were calculated from a t-test (two-sided) and p-value of the model was calcu-lated from a F-test. No correction for multiple comparisons was done.§ p < 0.0001, ‡ p < 0.001, † p < 0.01, ∗ p < 0.05574.2. Gestational age dependent PPG featuresIn the case of both PPG features, the differences are apparently observ-able from the first trimester of pregnancy when hypertension should cor-respond mostly to women who are hypertensive before becoming pregnant.So, I would suggest that these PPG features relations might be present inpregnancy independently of the hypertension category and gestational ageat which hypertension is detected.4.2.4 Skewness, spring constant, dicrotic notch anddiastolic peak positionSkewness, spring constant, dicrotic notch and diastolic peak position are allblood pressure related PPG features. An increase in blood pressure leadto an earlier and higher dicrotic notch and diastolic peak [48, 60, 61, 74]while decreasing the skewness and spring constant. In this subsection, thediastolic peak height is the augmentation index and the diastolic peak timeis the inverse of the arterial stiffness index (ASI).According to the table 4.7 and Figure 4.7, the dicrotic notch and diastolicpeak times are shorter in hypertension compared to normotension regardlessof the gestational age. The result is consistent with the theory that a higherblood pressure will lead to an increased wave reflection velocity causing itto be detected earlier and closer to the systolic peak.Regarding the dicrotic notch and diastolic peak height, the relation be-tween hypertension and normotension is not very clear. Both heights arerepresented as a percentage of the systolic peak height which might have af-fected the result. However, both heights tend to decrement in normotensionas gestational age increases as shown in table 4.7. More specifically accord-ing to Figures 4.1 and 4.2, both decrease until month 8 in normotension.As mentioned in the subsection 3.1.2, the dicrotic notch position wascalculated with the proposed algorithm, the delineator algorithm [37] andas the e wave location for comparison. No difference in the correlation,p-value and direction of the linear models were found regardless of the dif-ferent estimates obtained for each model. Whether one of the dicrotic notchdetection algorithms is slightly better for an specific porpuse as detectingblood pressure changes could be tested in a future study with a sensitivityanalysis as sobol indices.In the case of the skewness and spring constant, a decrease in hyperten-sion compared to normotension in pregnant women as expected is shown intable 4.7. The decreased skewness and spring constant in the hypertensivegroup compared to the normotensive group are apparently present regardlessof the gestational age.584.2. Gestational age dependent PPG featuresTable 4.7: Skewness, spring constant, dicrotic notch and diastolic peak positionvariations due to gestational age and blood pressure status: regression model re-sults.Normotensive Prehypertensive Hypertensive(reference) difference differencep rM2 4.67 ± 0.03§ 0.22 ± 0.06§ 0.61 ± 0.16§Arterial StiffnessIndex(s−1)MI -0.03 ± 0.006§ 0.03 ± 0.015∗ 0.11 ± 0.030§§ 0.22M2 193 ± 0.84§ -3 ± 2.19 -15 ± 5.57‡Dicrotic notchtime (ms)MI 0.19 ± 0.21 -1.11 ± 0.51† -2.60 ± 1.02†§ 0.16M2 61.6 ± 0.3§ -2.4 ± 0.7§ -2.4 ± 1.8AugmentationIndex (%)MI -0.7 ± 0.07§ 0.9 ± 0.16§ 1.2 ± 0.32§§ 0.11M2 58.3 ± 0.29§ -2.1 ± 0.77‡ -2.5 ± 1.9Dicrotic notchheight (%)MI -0.8 ± 0.07§ 1.0 ± 0.18§ 1.5 ± 0.36§§ 0.13M2 0.910 ± 0.004§ -0.020 ± 0.011∗ -0.055 ± 0.027†SkewnessMI 0.014 ± 0.001§ -0.007 ± 0.003‡ -0.003 ± 0.005§ 0.17M2 0.860 ± 0.010§ -0.024 ± 0.025 -0.089 ± 0.063Spring constant(%)MI 0.015 ± 0.002§ -0.009 ± 0.006 -0.043 ± 0.011§§ 0.15M2 indicates estimate at month 2 of gestation and MI, estimated monthly increment.The model regression coeficients are described as mean ± standard deviation. Normoten-sive visits are used as reference values. Prehypertensive and hypertensive visits are de-scribed with the difference from the normotensive reference value. P-values of regressioncoefficients were calculated from a t-test (two-sided) and p-value of the model was calcu-lated from a F-test. No correction for multiple comparisons was done.§ p < 0.0001, ‡ p < 0.001, † p < 0.01, ∗ p < 0.05594.2. Gestational age dependent PPG featuresFigure 4.7: ASI, gestational age and blood pressure group. Dashed line indicatesthe interquartile range and the solid line, the linear regression model.4.2.5 Area under the curveThe area under the curve refers to three measures: the area under the pulse,the area under the anacrotic phase and the area under the catacrotic phase.The maximum of each measure is 100% and the minimum is 0% becauseeach section was normalized before calculating the area under the curve.According to table 4.9 and Figures 4.1 and 4.2, the three features de-crease as gestational age increases in normotensive pregnant women. Theareas under the anacrotic phase and the catacrotic phase are calculated withnormalized time. When a given phase’s time decreases, the calculated ar-eas decrease too. The area under the pulse and the anacrotic phase areapparently more affected by blood pressure than by heart rate.If the changes in the area under the curve of the PPG could be extrap-olated to the arterial pressure waveform, then it can potentially become asignificant PPG feature. The reason is that the effect of hypertension, gesta-tional age or other factors measured in the PPG area under the curve couldbe used for a more accurate non-invasive estimation of the mean arterialpressure [4].604.3. ConclusionTable 4.8: SpO2 related features variations due to gestational age and blood pres-sure status: regression model results.Normotensive Prehypertensive Hypertensive(reference) difference differencep rM2 98.58 ± 0.03§ -0.12 ± 0.07∗ -0.52 ± 0.18‡Mean SpO2 (%)MI 0.01 ± 0.01 0.01 ± 0.02 0.10 ± 0.03‡‡ 0.04M2 0.83 ± 0.03§ 0.02 ± 0.08 0.51 ± 0.20‡SD SpO2 (%)MI -0.03 ± 0.01§ 0.02 ± 0.02 -0.10 ± 0.04‡§ 0.06M2 4.50 ± 0.19§ 0.86 ± 0.50∗ 4.54 ± 1.26§Time below 98%of SpO2 (s) MI -0.24 ± 0.05§ 0.01 ± 0.12 -0.82 ± 0.23§§ 0.08M2 2.19 ± 0.13§ 0.57 ± 0.33∗ 2.83 ± 0.83§Time below 96%of SpO2 (s) MI -0.09 ± 0.03‡ -0.02 ± 0.08 -0.50 ± 0.15§§ 0.06M2 indicates estimate at month 2 of gestation and MI, estimated monthly increment.The model regression coeficients are described as mean ± standard deviation. Normoten-sive visits are used as reference values. Prehypertensive and hypertensive visits are de-scribed with the difference from the normotensive reference value. P-values of regressioncoefficients were calculated from a t-test (two-sided) and p-value of the model was calcu-lated from a F-test. No correction for multiple comparisons was done.§ p < 0.0001, ‡ p < 0.001, † p < 0.01, ∗ p < 0.054.2.6 SpO2 related featuresFor the calculated SpO2 related features no apparent effect of gestationalage, hypertension or the interaction was found.4.3 ConclusionWe focused this 4th chapter in analyzing the effect of gestational age andblood pressure on the PPG signal. Completing the two final objectives ofthe thesis:• Assessing the effect of gestational age in the PPG signal in normoten-sive women in the 1st section. Specifically, creating a visual represen-tation of the PPG morphology throughout pregnancy. This visual rep-resentation is calculated as the average pulse per month of gestationalage in normotensive pregnant women using 5,072 PPG signals. Wehave shown how normal cardiovascular changes during pregnancy can614.3. ConclusionTable 4.9: Area under the curve variations due to gestational age and blood pressurestatus: regression model results.Normotensive Prehypertensive Hypertensive(reference) difference differencep rM2 53.30 ± 0.10§ 0.11 ± 0.27 0.35 ± 0.68Area under thepulse (%)MI -0.37 ± 0.03§ 0.23 ± 0.06§ 0.15 ± 0.12§ 0.15M2 52.24 ± 0.10§ 0.72 ± 0.25‡ 2.48 ± 0.64§Area under theanacrotic phase(%) MI -0.16 ± 0.02§ 0.14 ± 0.06† 0.42 ± 0.12§§ 0.22M2 41.73 ± 0.10§ -1.14 ± 0.26§ -3.0 ± 0.66§Area under thecatacrotic phase(%) MI -0.30 ± 0.03§ 0.25 ± 0.06§ 0.06 ± 0.12§ 0.18M2 indicates estimate at month 2 of gestation and MI, estimated monthly increment.The model regression coeficients are described as mean ± standard deviation. Normoten-sive visits are used as reference values. Prehypertensive and hypertensive visits are de-scribed with the difference from the normotensive reference value. P-values of regressioncoefficients were calculated from a t-test (two-sided) and p-value of the model was calcu-lated from a F-test. No correction for multiple comparisons was done.§ p < 0.0001, ‡ p < 0.001, † p < 0.01, ∗ p < 0.05induce changes in the PPG morphology. The obtained visual repre-sentation can be ideally used to estimate the direction of the changesduring pregnancy of unlimited PPG morphology features. The ob-tained visual representation clearly shows an evolution of the PPGmorphology through pregnancy, but it must be understood that it hasthe limitation of underestimating other common sources of variationof the PPG morphology.• Assessing the effect of gestational age, blood pressure and the interac-tion between them in the PPG signal in the 2nd section. Specifically,measuring this effect in the PPG features extracted in the 3rd chap-ter. The measured effect of the interaction between the blood pressurestatus and the gestational age in the PPG features provides an insightof how cardiovascular changes during pregnancy might differ betweenhypertensive, prehypertensive and normotensive women. For exam-ple, a decrease in the expected heart rate through pregnancy whenhypertensive, rather than an increase as when the pregnant women isnormotensive. The obtained results suggests that the expected car-diovascular changes during pregnancy when normotensive do not nec-624.3. Conclusionessarily apply when hypertensive. Another finding from this analysisis that the expected relation of the PPG features with blood pressurefound in non-pregnant subjects do not necessarily apply to pregnantwomen.From the 2nd section results, it is plausible that the visual representationof the PPG morphology through pregnancy obtained from normotensivewomen in the 1st section does not apply to hypertensive or prehypertensivewomen. Also from these obtained results, we recommend that PPG modelsto detect hypertension should consider gestational age as a variable and forthe training sample and in the case that the PPG model is developed froma sample of non-pregnant subjects, then it should be tested in pregnantwomen at different gestational ages before using the estimates provided.63Chapter 5ConclusionIn this chapter the objectives, contributions and limitations of this thesisare explored. It also includes a future work section emphasized towards thedevelopment of a PPG model for the detection of hypertension in pregnancy.5.1 Objectives, contributions and limitationsThis thesis aimed to increase the understanding of the effect of gestationalage on the PPG signal and its plausible impact in the development of PPGmodels to detect hypertension in pregnancy. We intented to achieve thisgeneral objective by first meting a series of short term objectives throughthe chapters of this thesis. The resulting contributions and limitations aredescribed from the initial to the final objective as follows:5.1.1 Pulse segmentation: peaks and valleys locationThe objective of segmenting the PPG signal into pulses allows the analysisof each cardiac cycle effect and is a necessary step for PPG features ex-traction and SQI implementation. Towards this objective, an algorithm forpulse segmentation was developed implementing concepts from Karlen’s al-gorithm [30]. The developed algorithm outperformed the pulse and systolicpeak location accuracy, sensitivity and specificity obtained with Karlen’salgorithm. The contributions and limitations of the developed pulse seg-mentation algorithm are explored through its 3 stages:1. Average pulse width estimation: A modified Karlen’s algorithm [30]was used. The modifications were done to improve the accuracy of theaverage pulse width estimation, resulting in an improved pulse locationby either Karlen’s original algorithm or the developed pulse locationalgorithm. The limitation of these modifications is an increased timeto output the average pulse width estimation.2. Pulse location: An algorithm for pulse location was developed calledmoving window algorithm. The moving window algorithm advantages645.1. Objectives, contributions and limitationsand limitations compared to Karlen’s algorithm [30] are:(a) The accuracy, sensitivity and specificity is increased. The pos-sible impreciseness of the average pulse width estimate does notaffect as much the performance of the moving window algorithmcompared to Karlen’s algorithm [30].(b) Pulses and systolic peaks are identified in the same algorithm.(c) The average pulse width estimate used is fixed from a selectedsample. This can minimize the average pulse width estimatesobtained from samples with motion artifacts with the limitationthat no updating would be performed when a significant heartrate change is present. Assuming that motion artifacts are morecommon than a significant heart rate change in a one minutePPG signal, then a fixed average pulse width is desirable.(d) Reduced number of operations mostly due to the fixed averagepulse width estimate.3. Pulse verification: An optional algorithm intended to be used off-linewhich can be implemented with a few lines of code.Summary: The developed pulse segmentation algorithm improved the ac-curacy, sensitivity and specificity for pulse and systolic peak location. Thisalgorithm is intended to be used off-line but the first two stages could possi-bly be used in real-time. As future work, a comparison against other pulseand systolic peak locations algorithms can be done. The obtained pulse lo-cations are then used for implementing the signal quality index (SQI), whichallows identifying the high-quality PPG pulses for further analysis.5.1.2 Signal quality index (SQI) implementationThe objective of implementing an SQI algorithm is to identify high-qualitypulses and PPG signals for further analysis. The SQI described by Karlen[30] is implemented using the obtained pulses location. No major modifica-tions were done other than the backward and forward calculation as part ofan off-line implementation.We compared the performance of the estimated SQI against the LGTSQI, the sensor provider’s model, resulting in 97% of the PPG signals iden-tified as high-quality by our algorithm being identified as high-quality by theLGT SQI as well. We suggest that the estimated SQI performs a reliableidentification of high-quality pulses and PPG signals. A limitation of the655.1. Objectives, contributions and limitationsestimated SQI performance analysis is that the LGT SQI was used for com-parison rather than a human labelled PPG dataset. An advantage is thatthe estimated and LGT SQI were compared in 14,123 PPG signals. It mustbe mentioned that Karlen’s SQI has been previously tested with its originalpulse location algorithm [30]. As future work, the PPG signals which wereidentified as high-quality by only one of the SQI algorithms can be reviewedto identify the differences. Also, the estimated SQI can be tested in Karlen’slabelled dataset to measure if there is an improvement in the classification ofhigh and low quality pulses by using the proposed pulse location algorithm.Summary: 97% of the PPG signals that were identified as high-qualitywith the implemented SQI algorithm were identified as high-quality with theLGT SQI as well. Only high-quality pulses and PPG signals identified bythe estimated SQI are considered for PPG features extraction and analysis.Based on this results, we would suggest that the majority of the PPG signalsused to extract and analyze PPG features are high-quality.5.1.3 PPG features extractionThe objective of the PPG features extraction is to measure the effect ofgestational age, blood pressure and the interaction between them in thePPG signal. For this analysis PPG features related to blood pressure, heartrate variability and blood oxygen saturation are extracted from the 11,412high-quality and>35 seconds long PPG signals. To extract the selected PPGfeatures, the following PPG characteristic parameters need to be located:dicrotic notch, diastolic peak, maximum of PPG first derivative and PPGsecond derivative waves (a, b and e).The dicrotic notch, diastolic peak and e location have in common theability to identify the most significant wave reflection in the PPG signal.From these 3 parameters, the dicrotic notch is more widely described inliterature and a number of algorithms have attempted to locate it. Oneof them is the delineator dicrotic notch algorithm, which had been testedin a dataset of 2,564 pulses annotated by trained engineers and approvedby medical experts in 36 arterial blood pressure (ABP) signals obtaining asensitivity and positive predictivity of 98.98% and 98.81%, respectively [37].Based in the high-performance of the delineator dicrotic notch algorithm,it was selected for comparison to our designed dicrotic notch detection al-gorithm. Therefore, the ability of the estimated dicrotic notch location toassess changes in blood pressure and gestational age is tested in the 4thchapter against 1) the estimated e location and 2) the delineator dicroticnotch location [37]. Where a linear model of the effect of gestational age,665.1. Objectives, contributions and limitationsblood pressure and the interaction between them in the dicrotic notch lo-cation is analyzed. Between the models built from the estimated dicroticnotch, e location and the delineator dicrotic notch no difference was foundin the models correlation, models p-value and variables effect direction. Wesuggest that there is no significant difference between the dicrotic notch de-tection of the developed and delineator algorithms. As future work, it can betested wether one of the dicrotic notch detection algorithms is slightly betterfor the specific detection of blood pressure or gestational age changes with asensitivity analysis as sobol indices. We also suggest that the dicrotic notchalgorithms performance can be compared in PPG labelled data. However,according to the linear models obtained in the 4th chapter, the estimateddiastolic peak location is apparently a better indicator of hypertension atdifferent gestational ages than the estimated dicrotic notch, e location anddelineator dicrotic notch. So, we suggest that future algorithms which aimto locate a significant wave reflection in the PPG signal for hypertension de-tection should prioritize the diastolic peak location over the dicrotic notchand e location.The maximum of the PPG first derivative, a and b location algorithmperformance was not tested, however since it is design using simple accepteddefinitions, it is suggested that its performance is equivalent to the perfor-mance of the pulse and systolic peak location.Summary: As a first step the location of the following PPG characteris-tic parameters was performed: dicrotic notch, diastolic peak, maximum ofPPG first derivative and PPG second derivative waves (a, b and e). Theperformance of the developed algorithm used to locate the dicrotic notchis comparable to the delineator dicrotic notch algorithm [37] which have ahigh-performance. The algorithms for the location of the diastolic peak,maximum of PPG first derivative and PPG second derivative waves (a, band e) were not directly tested but had an apparently high performance aswell. Based on further analysis we suggest that algorithms which aim tolocate a significant wave reflection in the PPG signal for hypertension de-tection should prioritize the location of the diastolic peak over the dicroticnotch and e wave. As a second step PPG features related to blood pressure,heart rate variability and blood oxygen saturation were extracted from the11,412 high-quality and >35 seconds long PPG signals. The obtained PPGfeatures were used to measure the effect of gestational age, blood pressureand the interaction between them in the PPG signal.675.1. Objectives, contributions and limitations5.1.4 Effect of gestational age, blood pressure and theinteraction between them in the PPG signal.The objective of assessing the gestational age effect in the PPG signal waspursued because we aim to build a PPG model to detect hypertension inpregnancy. If the characteristic evolution of the cardiovascular system dur-ing pregnancy significantly affects the PPG signal, then the gestational ageshould be considered either as a variable or as a criterion to select the train-ing sample of a PPG prediction model.As a first exploratory analysis the effect of gestational age in the PPGsignal was tested with an univariate linear regression between the PPG fea-tures and the gestational age. However, with this approach, we could notcompare the effect of gestational age with the effect of blood pressure in thePPG signal to conclude its significance. Despite this, we created a visualrepresentation of the changes in the PPG morphology through pregnancyusing 5,072 PPG signals from normotensive pregnant women, proving thatthe characteristic evolution of the cardiovascular system during pregnancyaffects the PPG morphology. This visual representation can be ideally usedto estimate the direction of the changes of unlimited PPG morphology fea-tures during pregnancy, however, it can not be used to conclude the clinicalsignificance of this changes. A limitation of this visual representation is thatit underestimates other sources of variation of the PPG morphology whichare described in section 1.1.2.As a next step, the effect of gestational age, blood pressure and theinteraction between them is measured to interpret the significance of thegestational age effect. For this purpose each PPG feature is fit to a linearmodel which input variables are gestational age, blood pressure and the in-teraction between them. According to the obtained results, we suggest thatgestational age should be considered as a variable and as a criterion to selectthe training sample for developing a PPG prediction model. The expectedeffect of blood pressure in some PPG features can significantly differ de-pending on gestational age. For example, in the first trimester, heart rateis increased in hypertension compared to normotension as in non-pregnantsubjects but in the third trimester the opposite relationship is found. Theseresults also suggest that PPG models that estimate blood pressure developedin non-pregnant subjects should be tested in pregnant women at differentgestational ages before using the estimates provided. Finally, the obtainedresults suggests that the expected cardiovascular system evolution throughpregnancy may differ depending on the woman’s blood pressure.Summary: The normal cardiovascular changes in pregnancy have an ob-685.2. Future workservable effect on the PPG signal. The gestational age is a significant vari-able and should be considered for the development of PPG models to detecthypertension. As a limitation of this study, we considered an extra numberof PPG signals from systolic hypertensive pregnant women. A PPG signalwas recorded in a subsequent visit if the systolic blood pressure was higheror equal to 140 mmHg. Another limitation is that the effect of hypertensionon the PPG signal was not analyzed by the type of hypertension.5.2 Future workA visual representation of how being hypertensive affects the PPG signalthrough pregnancy can be developed for comparison with the one of nor-motensive women. I suggest that this visual representation can be ideallycreated by categories of hypertension in pregnancy because depending onthe category, hypertension is or is not present before 20 weeks of gestation.Diastolic hypertension and systolic hypertension could also be compared.The observed variability between subjects of the PPG signal might bereduced by:• Adopting the current protocol for blood pressure measurement withthe sphygmomanometer (resting in a defined position) for the durationof PPG signal collection.• Collecting the PPG signal before measuring blood pressure with asphygmomanometer. If the blood pressure and the PPG signal arecollected at similar times, the occlusion of the brachial artery with theblood pressure cuff may affect the PPG signal.• Collecting information about any other factors that might induce vari-ability between subjects as gestational age, height, age and body massindex.Currently, efforts have been made to predict blood pressure from thePPG signal using machine learning algorithms. However, I would suggesttry to reduce variability by using a measurement protocol and that theinput should consider a sample of 1 minute per patient and should not besegmented into multiple ‘independent’ samples as it is commonly done inliterature [61, 70, 73]. It is also recommended to train, test and validate themachine learning models for the interpretations that can be done from theresults.695.2. Future workTo improve the understanding of the blood pressure and gestational ageeffect on the PPG signal, different types of hypertension could be identifiedand analyzed separately. For example for heart rate, we could test whetherthe association of increased heart rate with chronic hypertension is presenton gestational hypertension.We could also test wether the PPG signal of normotensive pregnantwomen who became hypertensive was different from the ones that did not.The PPG signal collected in first or subsequent visits might be used totest the probability that a pregnant women might have greater levels ofproteinuria. It could also be directly compared the PPG signal of chronichypertensive women during pregnancy, non-pregnant chronic hypertensivewomen and women with pregnancy induced hypertension.The CLIP intervention sample has the advantage of being a very com-plete sample to try to predict and/or understand hypertension in pregnancythrough the PPG signal. 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A big percentage of falsepositives is possible when the estimated average pulse width is around halfof the real average pulse width.% PPG − PPG s i g n a l which only c ont a in s complete p u l s e% pu l seLocs − Vector wi th p u l s e s l o c a t i o n% peakLocs − Vector wi th s y s t o l i c peaks l o c a t i o nT = length ( peakLocs ) ;s = PPG( peakLocs ) ;p = PPG( pulseLocs ) ;H1 = s ( 1 :T) − p ( 1 :T) ;H2 = s ( 2 :T−1) − p ( 1 :T−2);H3 = s ( 2 :T−1) − p ( 3 :T) ;H4 = p ( 2 :T−1) > p ( 1 :T−2);H5 = p ( 2 :T−1) > p ( 3 :T) ;HeightPeak = H2. /H1 ( 1 : end−2)<0.75 & H3. /H1 ( 3 : end)<0.75 & H4 & H5 ;H2 = s ( 1 :T−2) − p ( 2 :T−1);H3 = s ( 3 :T) − p ( 2 :T−1);H4 = s ( 2 :T−1) < s ( 1 :T−2);H5 = s ( 2 :T−1) < s ( 3 :T) ;HeightBeat = H2. /H1 ( 1 : end−2)<0.75 & H3. /H1 ( 3 : end)<0.75 & H4 & H5 ;A = HeightPeak | HeightBeat ;i f sum(A) > round(T∗0 . 1 )80Appendix A. Pulse verification algorithm codepeakLocs ( l o g i c a l ( [ 0 ;A; 0 ] ) ) = 0 ;pu l seLocs ( l o g i c a l ( [ 0 ;A ; 0 ; 0 ] ) ) = 0 ;peakLocs = peakLocs ( peakLocs >0);pu l seLocs = pulseLocs ( pulseLocs >0);end81
Thesis/Dissertation
eng
Biomedical Engineering
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Insights into the effect of blood pressure and pregnancy on the PPG signal
Text