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Monitoring sleep and sleep breathing disorders using pulse oximeter photoplethysmogram

University of British Columbia

We developed novel algorithms for monitoring sleep, sleep breathing disorder (SBD) and instantaneous respiratory rate (IRR) in children using the characterization of pulse oximetry photoplethysmogram (PPG). To evaluate the algorithms, we recorded the oxygen saturation (SpO₂) and PPG signals from 160 children using a phone-based oximeter consisting of a microcontroller-based pulse oximeter module interfacing a smartphone. This mobile oximeter was further developed to perform all processing on the smartphone through the audio interface. We evaluated the relative impact of SBD on sympathetic and parasympathetic activity in children through the characterization of PPG and concluded that sympathetic activity was higher in 30-second epochs with apnea/hypopnea event(s). We later characterized the SpO₂ pattern in SDB and then combined SpO₂ pattern characterization and PPG analysis to design a model with two binary logistic classifiers to identify the epochs with apnea/hypopnea events. We developed a novel model for identifying the cycles of random eye movement (REM) and non-REM of the overnight sleep based on the activity of cardiorespiratory system using the overnight PPG. We extracted the features associated with pulse rate variability (PRV), respiratory rate (RR), vascular tone and movement from PPG to build a model with two binary classifiers to identify wakefulness from sleep (wake/sleep classifier) and REM from non-REM sleep (non-REM/REM classifier). We also developed a novel algorithm for extracting the instantaneous respiratory rate (IRR) from PPG. The algorithm was performed in three steps: extraction of three respiratory-induced variation signals from PPG, estimation of IRR from each extracted respiratory-induced variation signal and fusion of IRR estimates. A time-frequency transform called synchrosqueezing transform (SST) was used to extract the respiratory-induced variation signals from PPG. Later, a second SST was applied to estimate IRR from respiratory-induced variation signals. To fuse IRR estimates, a novel algorithm was proposed. This study would expand the functionality of conventional pulse oximetry beyond the measurement of heart rate and SpO₂ to monitor sleep, to screen SBDs and measure the respiratory rate continuously and instantly.

Text

2018-07-26

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/66591

Biomedical Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/