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Healthcare"],"published-print":{"date-parts":[[2021,4,30]]},"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.<\/jats:p>","DOI":"10.1145\/3433987","type":"journal-article","created":{"date-parts":[[2021,2,10]],"date-time":"2021-02-10T10:53:22Z","timestamp":1612954402000},"page":"1-25","update-policy":"http:\/\/dx.doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":15,"title":["Machine Learning for Sleep Apnea Detection with Unattended Sleep Monitoring at Home"],"prefix":"10.1145","volume":"2","author":[{"given":"Stein","family":"Kristiansen","sequence":"first","affiliation":[{"name":"Department of Informatics, University of Oslo"}]},{"given":"Konstantinos","family":"Nikolaidis","sequence":"additional","affiliation":[{"name":"Department of Informatics, University of Oslo"}]},{"given":"Thomas","family":"Plagemann","sequence":"additional","affiliation":[{"name":"Department of Informatics, University of Oslo"}]},{"given":"Vera","family":"Goebel","sequence":"additional","affiliation":[{"name":"Department of Informatics, University of Oslo"}]},{"given":"Gunn Marit","family":"Traaen","sequence":"additional","affiliation":[{"name":"Oslo University Hospital, Rikshospitalet and University of Oslo"}]},{"given":"Britt","family":"\u00d8verland","sequence":"additional","affiliation":[{"name":"Lovisenberg Diakonale Hospital"}]},{"given":"Lars","family":"Aaker\u00f8y","sequence":"additional","affiliation":[{"name":"St. Olavs University Hospital and Norwegian University of Science and Technology"}]},{"given":"Tove-Elizabeth","family":"Hunt","sequence":"additional","affiliation":[{"name":"Oslo University Hospital, Rikshospitalet and University of Oslo"}]},{"given":"Jan P\u00e5l","family":"Loennechen","sequence":"additional","affiliation":[{"name":"Norwegian University of Science and Technology and St. Olavs University Hospital"}]},{"given":"Sigurd Loe","family":"Steinshamn","sequence":"additional","affiliation":[{"name":"St. Olavs University Hospital and Norwegian University of Science and Technology"}]},{"given":"Christina Holt","family":"Bendz","sequence":"additional","affiliation":[{"name":"Oslo University Hospital, Rikshospitalet"}]},{"given":"Ole-Gunnar","family":"Anfinsen","sequence":"additional","affiliation":[{"name":"Oslo University Hospital, Rikshospitalet"}]},{"given":"Lars","family":"Gullestad","sequence":"additional","affiliation":[{"name":"Oslo University Hospital, Rikshospitalet and University of Oslo"}]},{"given":"Harriet","family":"Akre","sequence":"additional","affiliation":[{"name":"Oslo University Hospital, Rikshospitalet and University of Oslo"}]}],"member":"320","published-online":{"date-parts":[[2021,2,9]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Nox Medical. 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Deep learning with noisy labels: Exploring techniques and remedies in medical image analysis. arXiv:1912.02911. Retrieved from https:\/\/arxiv.org\/abs\/1912.02911. 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."},{"key":"e_1_2_1_25_1","doi-asserted-by":"crossref","first-page":"74598","DOI":"10.1109\/ACCESS.2018.2882270","article-title":"Data mining for patient friendly apnea detection","volume":"6","author":"Kristiansen Stein","year":"2018","unstructured":"Stein Kristiansen , Mari S\u00f8nsteby Hugaas , Vera Goebel , Thomas Plagemann , Konstantinos Nikolaidis , and Knut Liest\u00f8l . 2018 . Data mining for patient friendly apnea detection . IEEE Access 6 (2018), 74598 -- 74615 . Stein Kristiansen, Mari S\u00f8nsteby Hugaas, Vera Goebel, Thomas Plagemann, Konstantinos Nikolaidis, and Knut Liest\u00f8l. 2018. Data mining for patient friendly apnea detection. IEEE Access 6 (2018), 74598--74615.","journal-title":"IEEE Access"},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2018.03.011"},{"key":"e_1_2_1_27_1","doi-asserted-by":"crossref","first-page":"71","DOI":"10.5664\/jcsm.5398","article-title":"Agreement in the scoring of respiratory events among international sleep centers for home sleep testing","volume":"12","author":"Magalang Ulysses J.","year":"2016","unstructured":"Ulysses J. Magalang , Erna S. Arnardottir , Ning-Hung Chen , Peter A. Cistulli , Thorarinn G\u00edslason , Diane Lim , Thomas Penzel , Richard Schwab , Sergio Tufik , Allan I. Pack , et\u00a0al. 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 . Ulysses J. Magalang, Erna S. Arnardottir, Ning-Hung Chen, Peter A. Cistulli, Thorarinn G\u00edslason, Diane Lim, Thomas Penzel, Richard Schwab, Sergio Tufik, Allan I. Pack, et\u00a0al. 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.","journal-title":"J. Clin. Sleep Med."},{"key":"e_1_2_1_28_1","doi-asserted-by":"crossref","first-page":"825","DOI":"10.1109\/JBHI.2018.2823265","article-title":"A review of obstructive sleep apnea detection approaches","volume":"23","author":"Mendonca Fabio","year":"2018","unstructured":"Fabio Mendonca , Sheikh Shanawaz Mostafa , Antonio G. Ravelo-Garc\u00eda , 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 . Fabio Mendonca, Sheikh Shanawaz Mostafa, Antonio G. Ravelo-Garc\u00eda, 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.","journal-title":"IEEE J. Biomed. Health Inf."},{"key":"e_1_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.3390\/s19224934"},{"key":"e_1_2_1_30_1","volume-title":"Proceedings of the 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 5234--5237","author":"Nov\u00e1k D.","year":"2008","unstructured":"D. Nov\u00e1k , 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 . D. Nov\u00e1k, 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."},{"key":"e_1_2_1_31_1","volume-title":"Computers in Cardiology","author":"Penzel Thomas","year":"2000","unstructured":"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. 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."},{"key":"e_1_2_1_32_1","volume-title":"Retrieved","year":"2011","unstructured":"PhysioNet. 2011 . SHHS Polysomnography Database . Retrieved January 28, 2015 from http:\/\/physionet.org\/physiobank\/database\/shhpsgdb\/. PhysioNet. 2011. SHHS Polysomnography Database. Retrieved January 28, 2015 from http:\/\/physionet.org\/physiobank\/database\/shhpsgdb\/."},{"key":"e_1_2_1_33_1","volume-title":"Retrieved","year":"2013","unstructured":"PhysioNet. 2013 . The MIT-BIH Polysomnography Database . Retrieved January 28, 2015 from http:\/\/physionet.org\/physiobank\/database\/slpdb\/. PhysioNet. 2013. The MIT-BIH Polysomnography Database. Retrieved January 28, 2015 from http:\/\/physionet.org\/physiobank\/database\/slpdb\/."},{"key":"e_1_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2017.01.001"},{"key":"e_1_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/3150226"},{"key":"e_1_2_1_36_1","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1109\/RBME.2017.2757899","article-title":"Medical devices for pediatric apnea monitoring and therapy: Past and new trends","volume":"10","author":"Pullano Salvatore Andrea","year":"2017","unstructured":"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 . 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.","journal-title":"IEEE Rev. Biomed. Eng."},{"key":"e_1_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1513\/pats.200709-155MG"},{"key":"e_1_2_1_38_1","volume-title":"Kording","author":"Saeb Sohrab","year":"2017","unstructured":"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. 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."},{"key":"e_1_2_1_39_1","doi-asserted-by":"crossref","first-page":"629","DOI":"10.1093\/sleep\/32.5.629","article-title":"Validation of a portable monitoring system for the diagnosis of obstructive sleep apnea syndrome","volume":"32","author":"Santos-Silva Rogerio","year":"2009","unstructured":"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 . 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.","journal-title":"Sleep"},{"key":"e_1_2_1_40_1","unstructured":"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. 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."},{"key":"e_1_2_1_41_1","volume-title":"Retrieved","author":"Sweetzpot Sweetzpot","year":"2020","unstructured":"Sweetzpot [n.d.]. Sweetzpot . Retrieved September 2020 from https:\/\/www.sweetzpot.com\/. Sweetzpot [n.d.]. Sweetzpot. Retrieved September 2020 from https:\/\/www.sweetzpot.com\/."},{"key":"e_1_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1056\/NEJM199903183401104"},{"key":"e_1_2_1_43_1","doi-asserted-by":"crossref","first-page":"100447","DOI":"10.1016\/j.ijcha.2019.100447","article-title":"Prevalence, risk factors, and type of sleep apnea in patients with paroxysmal atrial fibrillation","volume":"26","author":"Traaen Gunn Marit","year":"2020","unstructured":"Gunn Marit Traaen , Britt \u00d8verland , Lars Aaker\u00f8y , T. E. Hunt , Christina Bendz , L. Sande , Svend Aakhus , H. Zar\u00e9 , S. Steinshamn , Ole-Gunnar Anfinsen , et\u00a0al. 2020 . Prevalence, risk factors, and type of sleep apnea in patients with paroxysmal atrial fibrillation . IJC Heart Vasc. 26 (2020), 100447 . Gunn Marit Traaen, Britt \u00d8verland, Lars Aaker\u00f8y, T. E. Hunt, Christina Bendz, L. Sande, Svend Aakhus, H. Zar\u00e9, S. Steinshamn, Ole-Gunnar Anfinsen, et\u00a0al. 2020. Prevalence, risk factors, and type of sleep apnea in patients with paroxysmal atrial fibrillation. IJC Heart Vasc. 26 (2020), 100447.","journal-title":"IJC Heart Vasc."},{"key":"e_1_2_1_44_1","volume-title":"Proceedings of the 13th International Conference on Pattern Recognition","volume":"2","author":"Tumer Kagan","year":"1996","unstructured":"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. 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."},{"key":"e_1_2_1_45_1","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.mehy.2019.03.026","article-title":"A deep learning-based decision support system for diagnosis of OSAS using PTT signals","volume":"127","author":"Tuncer Seda Arslan","year":"2019","unstructured":"Seda Arslan Tuncer , Beyza Ak\u0131lotu , and Suat Toraman . 2019 . A deep learning-based decision support system for diagnosis of OSAS using PTT signals . Med. Hypoth. 127 (2019), 15 -- 22 . Seda Arslan Tuncer, Beyza Ak\u0131lotu, and Suat Toraman. 2019. A deep learning-based decision support system for diagnosis of OSAS using PTT signals. Med. Hypoth. 127 (2019), 15--22.","journal-title":"Med. Hypoth."},{"key":"e_1_2_1_46_1","doi-asserted-by":"crossref","unstructured":"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. 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.","DOI":"10.1088\/1361-6579\/aaafb8"},{"key":"e_1_2_1_47_1","doi-asserted-by":"crossref","first-page":"2354","DOI":"10.1109\/JBHI.2018.2886064","article-title":"Automated sleep apnea detection in raw respiratory signals using long short-term memory neural networks","volume":"23","author":"Steenkiste Tom Van","year":"2018","unstructured":"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 . 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.","journal-title":"IEEE J. Biomed. 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