Two-Stream Convolutional Neural Networks for Breathing Pattern Classification: Real-Time Monitoring of Respiratory Disease Patients
department or school: Medicine, Faculty of
department or school: Non UBC
department or school: Pathology and Laboratory Medicine, Department of
data provider: DSpace
identifier citation: Bioengineering 11 (7): 709 (2024)
contributor: International Collaboration on Repair Discoveries
creator: Park, Jinho
creator: Nguyen, Thien
creator: Park, Soongho
creator: Hill, Brian
creator: Shadgan, Babak
creator: Gandjbakhche, Amir
issued: 2024-08-02T19:52:13Z
issued: 2024-07-12
description: A two-stream convolutional neural network (TCNN) for breathing pattern classification has been devised for the continuous monitoring of patients with infectious respiratory diseases. The TCNN consists of a convolutional neural network (CNN)-based autoencoder and classifier. The encoder of the autoencoder generates deep compressed feature maps, which contain the most important information constituting data. These maps are concatenated with feature maps generated by the classifier to classify breathing patterns. The TCNN, single-stream CNN (SCNN), and state-of-the-art classification models were applied to classify four breathing patterns: normal, slow, rapid, and breath holding. The input data consisted of chest tissue hemodynamic responses measured using a wearable near-infrared spectroscopy device on 14 healthy adult participants. Among the classification models evaluated, random forest had the lowest classification accuracy at 88.49%, while the TCNN achieved the highest classification accuracy at 94.63%. In addition, the proposed TCNN performed 2.6% better in terms of classification accuracy than an SCNN (without an autoencoder). Moreover, the TCNN mitigates the issue of declining learning performance with increasing network depth, as observed in the SCNN model. These results prove the robustness of the TCNN in classifying breathing patterns despite using a significantly smaller number of parameters and computations compared to state-of-the-art classification models.
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/88859
note: Citation: Park, J.; Nguyen, T.; Park, S.;Hill, B.; Shadgan, B.; Gandjbakhche, A.Two-Stream Convolutional NeuralNetworks for Breathing PatternClassification: Real-Time Monitoringof Respiratory Disease Patients.Bioengineering 2024, 11, 709.https://doi.org/10.3390/bioengineering11070709Academic Editor: Luca MesinReceived: 29 May 2024Revised: 26 June 2024Accepted: 11 July 2024Published: 12 July 2024Copyright: © 2024 by the authors.Licensee MDPI, Basel, Switzerland.This article is an open access articledistributed under the terms andconditions of the Creative CommonsAttribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).bioengineeringArticleTwo-Stream Convolutional Neural Networks for BreathingPattern Classification: Real-Time Monitoring of RespiratoryDisease PatientsJinho Park 1 , Thien Nguyen 1, Soongho Park 1,2, Brian Hill 1, Babak Shadgan 3,4 and Amir Gandjbakhche 1,*1 Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutesof Health, 49 Convent Dr., Bethesda, MD 20894, USA; jinho.park@nih.gov (J.P.); thien.nguyen4@nih.gov (T.N.);soongho.park@nih.gov (S.P.); brian.hill@nih.gov (B.H.)2 National Heart, Lung and Blood Institute, National Institutes of Health, 10 Center Dr.,Bethesda, MD 20892, USA3 Implantable Biosensing Laboratory, International Collaboration on Repair Discoveries,Vancouver, BC V5Z 1M9, Canada; babak.shadgan@ubc.ca4 Department of Pathology & Laboratory Medicine, University of British Columbia,Vancouver, BC V6T 1Z7, Canada* Correspondence: gandjbaa@mail.nih.govAbstract: A two-stream convolutional neural network (TCNN) for breathing pattern classificationhas been devised for the continuous monitoring of patients with infectious respiratory diseases.The TCNN consists of a convolutional neural network (CNN)-based autoencoder and classifier.The encoder of the autoencoder generates deep compressed feature maps, which contain the mostimportant information constituting data. These maps are concaten…
has type: Article
is shown at: 10.14288/1.0444979
language: eng
peer review status: Reviewed
provider: Vancouver : University of British Columbia Library
publisher: Multidisciplinary Digital Publishing Institute
publisher DOI: 10.3390/bioengineering11070709
rights: CC BY 4.0
rights URI: https://creativecommons.org/licenses/by/4.0/
scholar level: Faculty
scholar level: Researcher
subject: COVID-19
subject: deep learning
subject: convolutional neural network
subject: respiratory disease
subject: NIRS
subject: wearable device
type: Text
identifier URI: http://hdl.handle.net/2429/88859