research-article

Machine Learning for Sleep Apnea Detection with Unattended Sleep Monitoring at Home

Authors:

Stein Kristiansen,

Konstantinos Nikolaidis,

Thomas Plagemann,

Gunn Marit Traaen,

Britt Øverland,

Tove-Elizabeth Hunt,

Jan Pål Loennechen,

Sigurd Loe Steinshamn,

Christina Holt Bendz,

Ole-Gunnar Anfinsen,

Lars Gullestad,

Harriet AkreAuthors Info & Claims

ACM Transactions on Computing for Healthcare , Volume 2, Issue 2

Article No.: 14, Pages 1 - 25

https://doi.org/10.1145/3433987

Published: 09 February 2021 Publication History

Abstract

Sleep apnea is a common and strongly under-diagnosed severe sleep-related respiratory disorder with periods of disrupted or reduced breathing during sleep. To diagnose sleep apnea, sleep data are collected with either polysomnography or polygraphy and scored by a sleep expert. We investigate in this work the use of supervised machine learning to automate the analysis of polygraphy data from the A3 study containing more than 7,400 hours of sleep monitoring data from 579 patients. We conduct a systematic comparative study of classification performance and resource use with different combinations of 27 classifiers and four sleep signals. The classifiers achieve up to 0.8941 accuracy (kappa: 0.7877) when using all four signal types simultaneously and up to 0.8543 accuracy (kappa: 0.7080) with only one signal, i.e., oxygen saturation. Methods based on deep learning outperform other methods by a large margin. All deep learning methods achieve nearly the same maximum classification performance even when they have very different architectures and sizes. When jointly accounting for classification performance, resource consumption and the ability to achieve with less training data high classification performance, we find that convolutional neural networks substantially outperform the other classifiers.

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Cited By

Dritsas ETrigka M(2024)Utilizing Multi-Class Classification Methods for Automated Sleep Disorder PredictionInformation10.3390/info1508042615:8(426)Online publication date: 23-Jul-2024
https://doi.org/10.3390/info15080426
Abd-alrazaq AAslam HAlSaad RAlsahli MAhmed ADamseh RAziz SSheikh J(2024)Detection of Sleep Apnea Using Wearable AI: Systematic Review and Meta-AnalysisJournal of Medical Internet Research10.2196/5818726(e58187)Online publication date: 10-Sep-2024
https://doi.org/10.2196/58187
Pourpanah FEtemad A(2024)Exploring the Landscape of Ubiquitous In-home Health Monitoring: A Comprehensive SurveyACM Transactions on Computing for Healthcare10.1145/36708545:4(1-43)Online publication date: 23-Oct-2024
https://dl.acm.org/doi/10.1145/3670854
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Index Terms

Machine Learning for Sleep Apnea Detection with Unattended Sleep Monitoring at Home
1. Applied computing
  1. Life and medical sciences
    1. Health informatics
2. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
    2. Machine learning approaches
      1. Neural networks

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Published In

cover image ACM Transactions on Computing for Healthcare

ACM Transactions on Computing for Healthcare Volume 2, Issue 2

April 2021

226 pages

EISSN:2637-8051

DOI:10.1145/3446675

Editors:
Insup Lee
University of Pennsylvania, USA
,
John A. Stankovic
University of Virginia, USA

Issue’s Table of Contents

Copyright © 2021 ACM.

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Publication History

Published: 09 February 2021

Accepted: 01 November 2020

Revised: 01 October 2020

Received: 01 July 2020

Published in HEALTH Volume 2, Issue 2

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Oslo University Hospital
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Cited By

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https://doi.org/10.3390/info15080426
Abd-alrazaq AAslam HAlSaad RAlsahli MAhmed ADamseh RAziz SSheikh J(2024)Detection of Sleep Apnea Using Wearable AI: Systematic Review and Meta-AnalysisJournal of Medical Internet Research10.2196/5818726(e58187)Online publication date: 10-Sep-2024
https://doi.org/10.2196/58187
Pourpanah FEtemad A(2024)Exploring the Landscape of Ubiquitous In-home Health Monitoring: A Comprehensive SurveyACM Transactions on Computing for Healthcare10.1145/36708545:4(1-43)Online publication date: 23-Oct-2024
https://dl.acm.org/doi/10.1145/3670854
Krithik KPadmashree PNayak NKushal SBhandarkar RShivaprakasha K(2024)Detection of Cardiac Arrhythmias in Patients with Obstructive Sleep Apnea using Machine Learning and Deep Learning Algorithms2024 Second International Conference on Data Science and Information System (ICDSIS)10.1109/ICDSIS61070.2024.10594270(1-8)Online publication date: 17-May-2024
https://doi.org/10.1109/ICDSIS61070.2024.10594270
Alarcón ÁGaiduk MMadrid NSeepold R(2024)A survey on pre-training requirements for deep learning models to detect obstructive sleep apnea eventsProcedia Computer Science10.1016/j.procs.2023.10.376225:C(3805-3812)Online publication date: 4-Mar-2024
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Alarcón ÁMadrid NSeepold ROrtega J(2023)Obstructive sleep apnea event detection using explainable deep learning models for a portable monitorFrontiers in Neuroscience10.3389/fnins.2023.115590017Online publication date: 14-Jul-2023
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Khandelwal SSalankar NMian Qaisar SUpadhyay JPławiak P(2023)ECG based apnea detection by multirate processing hybrid of wavelet-empirical decomposition Hjorth features extraction and neural networksPLOS ONE10.1371/journal.pone.029361018:11(e0293610)Online publication date: 2-Nov-2023
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Kristiansen SNikolaidis KPlagemann TGoebel VTraaen GØverland BAkerøy LHunt TLoennechen JSteinshamn SBendz CAnfinsen OGullestad LAkre H(2023)A clinical evaluation of a low-cost strain gauge respiration belt and machine learning to detect sleep apneaSmart Health10.1016/j.smhl.2023.10037327(100373)Online publication date: Mar-2023
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Barika RElphick HLei NRazaghi HFaust O(2022)Environmental Benefits of Sleep Apnoea Detection in the Home EnvironmentProcesses10.3390/pr1009173910:9(1739)Online publication date: 1-Sep-2022
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