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.

References

[1]

2020. Nox T3 Sleep Monitor, Nox Medical. Retrieved June 16, 2020 from http://noxmedical.com/products/nox-t3-sleep-monitor.

[2]

Daniel Álvarez, Ana Cerezo-Hernández, Andrea Crespo, Gonzalo C. Gutiérrez-Tobal, Fernando Vaquerizo-Villar, Verónica Barroso-García, Fernando Moreno, C. Ainhoa Arroyo, Tomás Ruiz, Roberto Hornero, et al. 2020. A machine learning-based test for adult sleep apnoea screening at home using oximetry and airflow. Sci. Rep. 10, 1 (2020), 1--12.

[3]

Daniel Álvarez, Gonzalo César Gutiérrez-Tobal, Fernando Vaquerizo-Villar, Verónica Barroso-García, A. Crespo, C. A. Arroyo, F. Del Campo, and R. Hornero. 2016. Automated analysis of unattended portable oximetry by means of Bayesian neural networks to assist in the diagnosis of sleep apnea. In Proceedings of the 2016 Global Medical Engineering Physics Exchanges/Pan American Health Care Exchanges (GMEPE/PAHCE’16). IEEE, 1--4.

[4]

Diego Alvarez-Estevez and Vicente Moret-Bonillo. 2015. Computer-assisted diagnosis of the sleep apnea-hypopnea syndrome: A review. Sleep Disorders 2015 (2015), 237878--237878.

[5]

Shun-ichi Amari, Naotake Fujita, and Shigeru Shinomoto. 1992. Four types of learning curves. Neural Comput. 4, 4 (1992), 605--618.

Digital Library

[6]

Brandon Ballinger, Johnson Hsieh, Avesh Singh, Nimit Sohoni, Jack Wang, Geoffrey H. Tison, Gregory M. Marcus, Jose M. Sanchez, Carol Maguire, Jeffrey E. Olgin, et al. 2018. DeepHeart: Semi-supervised sequence learning for cardiovascular risk prediction. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence.

[7]

Nannapas Banluesombatkul, Thanawin Rakthanmanon, and Theerawit Wilaiprasitporn. 2018. Single channel ECG for obstructive sleep apnea severity detection using a deep learning approach. In Proceedings of the IEEE Region 10 Conference (TENCON’18). IEEE, 2011--2016.

[8]

Adam V. Benjafield, Najib T. Ayas, Peter R. Eastwood, Raphael Heinzer, Mary S. M. Ip, Mary J. Morrell, Carlos M. Nunez, Sanjay R. Patel, Thomas Penzel, Jean-Louis Pépin, et al. 2019. Estimation of the global prevalence and burden of obstructive sleep apnoea: A literature-based analysis. Lancet Respir. Med. 7, 8 (2019), 687--698.

[9]

Richard B. Berry, Rita Brooks, Charlene E. Gamaldo, Susan M. Harding, C. Marcus, Bradley V. Vaughn, et al. 2012. The AASM manual for the scoring of sleep and associated events. Rules, Terminology and Technical Specifications, Vol. 176. American Academy of Sleep Medicine, Darien, IL.

[10]

Bitalino [n.d.]. Bitalino. Retrieved Setpember 2020 from http://bitalino.com/en/.

[11]

Alyssa Cairns, Emerson Wickwire, Edward Schaefer, and David Nyanjom. 2014. A pilot validation study for the NOX T3 TM portable monitor for the detection of OSA. Sleep Breathing 18, 3 (2014), 609--614.

[12]

Yuan Chang, Liyue Xu, Fang Han, Brendan T. Keenan, Elizabeth Kneeland-Szanto, Rongbao Zhang, Wei Zhang, Yongbo Yu, Yuhua Zuo, Allan I. Pack, and Samuel T. Kuna. 2019. Validation of the Nox-T3 portable monitor for diagnosis of obstructive sleep Apnea in patients with chronic obstructive pulmonary disease. J. Clin. Sleep Med. 15, 04 (2019), 587--596.

[13]

Jacob Cohen. 1960. A coefficient of agreement for nominal scales. Educ. Psychol. Meas. 20, 1 (1960), 37--46.

[14]

Nancy A. Collop, Sharon L. Tracy, Vishesh Kapur, Reena Mehra, David Kuhlmann, Sam A. Fleishman, and Joseph M. Ojile. 2011. Obstructive sleep apnea devices for out-of-center (OOC) testing: Technology evaluation. J. Clin. Sleep Med. 7, 5 (2011), 531--548.

[15]

Oliver Faust, U. Rajendra Acharya, E. Y. K. Ng, and Hamido Fujita. 2016. A review of ECG-based diagnosis support systems for obstructive sleep apnea. J. Mech. Med. Biol. 16, 01 (2016), 1640004.

[16]

E. Finnsson, S. A. E. Jonsson, H. Ragnarsdottir, H. M. Prainsson, H. Helgadottir, J. S. Agustsson, A. Wellman, and S. A. Sands. 2019. Respiratory inductance plethysmography for the reliable assessment of ventilation and sleep apnea phenotypes in the presence of oral breathing. In Sleep Medicine, Vol. 64. Elsevier, Amsterdam, Netherlands, S115--S116.

[17]

W. Ward Flemons, Michael R. Littner, James A. Rowley, Peter Gay, W. McDowell Anderson, David W. Hudgel, R. Douglas McEvoy, and Daniel I. Loube. 2003. Home diagnosis of sleep apnea: A systematic review of the literature: An evidence review cosponsored by the American Academy of Sleep Medicine, the American College of Chest Physicians, and the American Thoracic Society. Chest 124, 4 (2003), 1543--1579.

[18]

Eva García-Martín. 2020. Energy Efficiency in Machine Learning: Approaches to Sustainable Data Stream Mining. Ph.D. dissertation. Blekinge Tekniska Högskola.

[19]

Gonzalo C. Gutiérrez-Tobal, Daniel Álvarez, Andrea Crespo, Félix del Campo, and Roberto Hornero. 2018. Evaluation of machine-learning approaches to estimate sleep apnea severity from at-home oximetry recordings. IEEE J. Biomed. Health Inf. 23, 2 (2018), 882--892.

[20]

Rim Haidar, Stephen McCloskey, Irena Koprinska, and Bryn Jeffries. 2018. Convolutional neural networks on multiple respiratory channels to detect hypopnea and obstructive apnea events. In Proceedings of the 2018 International Joint Conference on Neural Networks (IJCNN’18). IEEE, 1--7.

[21]

Joel Hestness, Sharan Narang, Newsha Ardalani, Gregory Diamos, Heewoo Jun, Hassan Kianinejad, Md Patwary, Mostofa Ali, Yang Yang, and Yanqi Zhou. 2017. Deep learning scaling is predictable, empirically. arXiv:1712.00409. Retrieved from https://arxiv.org/abs/1712.00409.

[22]

Kurt Hornik, Maxwell Stinchcombe, and Halbert White. 1989. Multilayer feedforward networks are universal approximators. Neural Netw. 2, 5 (1989), 359--366.

[23]

Q. R. Huang, Z. Qin, S. Zhang, and C. M. Chow. 2008. Clinical patterns of obstructive sleep apnea and its comorbid conditions: A data mining approach. J. Clin. Sleep Med. 4, 6 (2008), 543--550.

[24]

Davood Karimi, Haoran Dou, Simon K. Warfield, and Ali Gholipour. 2019. Deep learning with noisy labels: Exploring techniques and remedies in medical image analysis. arXiv:1912.02911. Retrieved from https://arxiv.org/abs/1912.02911.

[25]

Stein Kristiansen, Mari Sønsteby Hugaas, Vera Goebel, Thomas Plagemann, Konstantinos Nikolaidis, and Knut Liestøl. 2018. Data mining for patient friendly apnea detection. IEEE Access 6 (2018), 74598--74615.

[26]

Kunyang Li, Weifeng Pan, Yifan Li, Qing Jiang, and Guanzheng Liu. 2018. A method to detect sleep apnea based on deep neural network and hidden markov model using single-lead ECG signal. Neurocomputing 294 (2018), 94--101.

[27]

Ulysses J. Magalang, Erna S. Arnardottir, Ning-Hung Chen, Peter A. Cistulli, Thorarinn Gíslason, Diane Lim, Thomas Penzel, Richard Schwab, Sergio Tufik, Allan I. Pack, et al. 2016. Agreement in the scoring of respiratory events among international sleep centers for home sleep testing. J. Clin. Sleep Med. 12, 1 (2016), 71--77.

[28]

Fabio Mendonca, Sheikh Shanawaz Mostafa, Antonio G. Ravelo-García, Fernando Morgado-Dias, and Thomas Penzel. 2018. A review of obstructive sleep apnea detection approaches. IEEE J. Biomed. Health Inf. 23, 2 (2018), 825--837.

[29]

Sheikh Shanawaz Mostafa, Fábio Mendonça, Antonio G. Ravelo-García, and Fernando Morgado-Dias. 2019. A systematic review of detecting sleep apnea using deep learning. Sensors 19, 22 (2019), 4934.

[30]

D. Novák, K. Mucha, and Tarik Al-Ani. 2008. Long short-term memory for apnea detection based on heart rate variability. In Proceedings of the 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 5234--5237.

[31]

Thomas Penzel, George B. Moody, Roger G. Mark, Ary L. Goldberger, and J. Hermann Peter. 2000. The apnea-ECG database. In Computers in Cardiology 2000, Vol. 27. IEEE, 255--258.

[32]

PhysioNet. 2011. SHHS Polysomnography Database. Retrieved January 28, 2015 from http://physionet.org/physiobank/database/shhpsgdb/.

[33]

PhysioNet. 2013. The MIT-BIH Polysomnography Database. Retrieved January 28, 2015 from http://physionet.org/physiobank/database/slpdb/.

[34]

Nuno Pombo, Nuno Garcia, and Kouamana Bousson. 2017. Classification techniques on computerized systems to predict and/or to detect Apnea: A systematic review. Comput. Methods Progr. Biomed. 140 (2017), 265--274.

Digital Library

[35]

Samira Pouyanfar, Saad Sadiq, Yilin Yan, Haiman Tian, Yudong Tao, Maria Presa Reyes, Mei-Ling Shyu, Shu-Ching Chen, and S. S. Iyengar. 2018. A survey on deep learning: Algorithms, techniques, and applications. ACM Comput. Surv. 51, 5 (2018), 1--36.

Digital Library

[36]

Salvatore Andrea Pullano, Ifana Mahbub, Maria Giovanna Bianco, Samira Shamsir, Syed Kamrul Islam, Mark S. Gaylord, Vichien Lorch, and Antonino S. Fiorillo. 2017. Medical devices for pediatric apnea monitoring and therapy: Past and new trends. IEEE Rev. Biomed. Eng. 10 (2017), 199--212.

[37]

N. M. Punjabi. 2008. The epidemiology of adult obstructive sleep apnea. Proc. Am. Thorac. Soc. 5, 2 (2008), 136--143.

[38]

Sohrab Saeb, Luca Lonini, Arun Jayaraman, David C. Mohr, and Konrad P. Kording. 2017. The need to approximate the use-case in clinical machine learning. Gigascience 6, 5 (2017), gix019.

[39]

Rogerio Santos-Silva, Denis E. Sartori, Viviane Truksinas, Eveli Truksinas, Fabiana F. F. D. Alonso, Sergio Tufik, and Lia R. A. Bittencourt. 2009. Validation of a portable monitoring system for the diagnosis of obstructive sleep apnea syndrome. Sleep 32, 5 (2009), 629--636.

[40]

Emma Strubell, Ananya Ganesh, and Andrew McCallum. 2019. Energy and policy considerations for deep learning in NLP. arXiv:1906.02243. Retrieved from https://arxiv.org/abs/1906.02243.

[41]

Sweetzpot [n.d.]. Sweetzpot. Retrieved September 2020 from https://www.sweetzpot.com/.

[42]

J. Teran-Santos, A. Jimenez-Gomez, and J. Cordero-Guevara. 1999. The association between sleep apnea and the risk of traffic accidents. New Engl. J. Med. 340, 11 (1999), 847--851.

[43]

Gunn Marit Traaen, Britt Øverland, Lars Aakerøy, T. E. Hunt, Christina Bendz, L. Sande, Svend Aakhus, H. Zaré, S. Steinshamn, Ole-Gunnar Anfinsen, et al. 2020. Prevalence, risk factors, and type of sleep apnea in patients with paroxysmal atrial fibrillation. IJC Heart Vasc. 26 (2020), 100447.

[44]

Kagan Tumer and Joydeep Ghosh. 1996. Estimating the Bayes error rate through classifier combining. In Proceedings of the 13th International Conference on Pattern Recognition, Vol. 2. IEEE, 695--699.

[45]

Seda Arslan Tuncer, Beyza Akılotu, and Suat Toraman. 2019. A deep learning-based decision support system for diagnosis of OSAS using PTT signals. Med. Hypoth. 127 (2019), 15--22.

[46]

M. B. Uddin, C. M. Chow, and S. W. Su. 2018. Classification methods to detect sleep apnea in adults based on respiratory and oximetry signals: A systematic review. Physiol. Meas. 39, 3 (2018), 03TR01.

[47]

Tom Van Steenkiste, Willemijn Groenendaal, Dirk Deschrijver, and Tom Dhaene. 2018. Automated sleep apnea detection in raw respiratory signals using long short-term memory neural networks. IEEE J. Biomed. Health Inf. 23, 6 (2018), 2354--2364.

[48]

Yequan Wang, Minlie Huang, Xiaoyan Zhu, and Li Zhao. 2016. Attention-based LSTM for aspect-level sentiment classification. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. 606--615.

[49]

T. Young, J. Skatrud, and P. E. Peppard. 2004. Risk factors for obstructive sleep apnea in adults. J. Am. Med. Am. 291, 16 (2004), 2013--2016.

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
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
https://dl.acm.org/doi/10.1016/j.procs.2023.10.376
Show More Cited By

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

Recommendations

Contactless Sleep Apnea Detection on Smartphones
MobiSys '15: Proceedings of the 13th Annual International Conference on Mobile Systems, Applications, and Services

We present a contactless solution for detecting sleep apnea events on smartphones. To achieve this, we introduce a novel system that monitors the minute chest and abdomen movements caused by breathing on smartphones. Our system works with the phone away ...
Effect of CPAP therapy on daytime cardiovascular regulations in patients with obstructive sleep apnea

Obstructive sleep apnea (OSA) is a sleep disorder with a high prevalence that causes pathological changes in cardiovascular regulation during the night and also during daytime. We investigated whether the treatment of OSA at night by means of continuous ...
RF powered sleep apnea monitoring system
PETRA '15: Proceedings of the 8th ACM International Conference on PErvasive Technologies Related to Assistive Environments

Sleep Apnea is a chronic and widespread problem that is characterized by periods of pauses in breathing during sleep. It can lead to several life threatening conditions if left undiagnosed. Conventional methods of diagnosing sleep apnea involve ...

Comments

Information & Contributors

Information

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.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

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

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed

Funding Sources

Oslo University Hospital
University of Oslo
The Norwegian Research Council
The Norwegian Health Association

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

13
Total Citations
View Citations
707
Total Downloads

Downloads (Last 12 months)143
Downloads (Last 6 weeks)5

Reflects downloads up to 28 Jul 2024

Other Metrics

View Author Metrics

Citations

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
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
https://dl.acm.org/doi/10.1016/j.procs.2023.10.376
Sandhu MSilvera-Tawil DBorges PZhang QKusy B(2024)Internet of robotic things for independent living: Critical analysis and future directionsInternet of Things10.1016/j.iot.2024.10112025(101120)Online publication date: Apr-2024
https://doi.org/10.1016/j.iot.2024.101120
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
https://doi.org/10.3389/fnins.2023.1155900
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
https://doi.org/10.1371/journal.pone.0293610
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
https://doi.org/10.1016/j.smhl.2023.100373
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
https://doi.org/10.3390/pr10091739
Adhikary RLodhavia DFrancis CPatil RSrivastava TKhanna PBatra NBreda JPeplinski JPatel S(2022)SpiroMask: Measuring Lung Function Using Consumer-Grade MasksACM Transactions on Computing for Healthcare10.1145/35701674:1(1-34)Online publication date: 3-Nov-2022
https://dl.acm.org/doi/10.1145/3570167
Akbari FSartipi KArcher N(2022)Synthetic Behavior Sequence Generation Using Generative Adversarial NetworksACM Transactions on Computing for Healthcare10.1145/35639504:1(1-23)Online publication date: 29-Sep-2022
https://dl.acm.org/doi/10.1145/3563950
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

HTML Format

View this article in HTML Format.

Media

Figures

Other

Tables

View Issue’s Table of Contents