{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T21:58:46Z","timestamp":1786139926469,"version":"3.56.0"},"reference-count":93,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2019,11,12]],"date-time":"2019-11-12T00:00:00Z","timestamp":1573516800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Portuguese Foundation for Science and Technology","award":["Projeto Estrat\u00e9gico UID\/EEA\/50009\/2019"],"award-info":[{"award-number":["Projeto Estrat\u00e9gico UID\/EEA\/50009\/2019"]}]},{"name":"ARDITI - Ag\u00eancia Regional para o Desenvolvimento da Investiga\u00e7\u00e3o, Tecnologia e Inova\u00e7\u00e3o","award":["Project M1420-09-5369-FSE-000001-PhD Studentship"],"award-info":[{"award-number":["Project M1420-09-5369-FSE-000001-PhD Studentship"]}]},{"name":"MITIExcell - Excelencia Internacional de IDT&amp;I NAS TIC  provided by the Regional Government of Madeira","award":["Project Number M1420-01-01450FEDER0000002"],"award-info":[{"award-number":["Project Number M1420-01-01450FEDER0000002"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Sleep apnea is a sleep related disorder that significantly affects the population. Polysomnography, the gold standard, is expensive, inaccessible, uncomfortable and an expert technician is needed to score. Numerous researchers have proposed and implemented automatic scoring processes to address these issues, based on fewer sensors and automatic classification algorithms. Deep learning is gaining higher interest due to database availability, newly developed techniques, the possibility of producing machine created features and higher computing power that allows the algorithms to achieve better performance than the shallow classifiers. Therefore, the sleep apnea research has currently gained significant interest in deep learning. The goal of this work is to analyze the published research in the last decade, providing an answer to the research questions such as how to implement the different deep networks, what kind of pre-processing or feature extraction is needed, and the advantages and disadvantages of different kinds of networks. The employed signals, sensors, databases and implementation challenges were also considered. A systematic search was conducted on five indexing services from 2008\u20132018. A total of 255 papers were found and 21 were selected by considering the inclusion and exclusion criteria, using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) approach.<\/jats:p>","DOI":"10.3390\/s19224934","type":"journal-article","created":{"date-parts":[[2019,11,13]],"date-time":"2019-11-13T09:11:27Z","timestamp":1573636287000},"page":"4934","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":170,"title":["A Systematic Review of Detecting Sleep Apnea Using Deep Learning"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7677-0971","authenticated-orcid":false,"given":"Sheikh Shanawaz","family":"Mostafa","sequence":"first","affiliation":[{"name":"Instituto Superior T\u00e9cnico, Universidade de Lisboa, 1049-001 Lisboa, Portugal"},{"name":"Madeira Interactive Technologies Institute, 9020-105 Funchal, Portugal"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5107-3248","authenticated-orcid":false,"given":"F\u00e1bio","family":"Mendon\u00e7a","sequence":"additional","affiliation":[{"name":"Instituto Superior T\u00e9cnico, Universidade de Lisboa, 1049-001 Lisboa, Portugal"},{"name":"Madeira Interactive Technologies Institute, 9020-105 Funchal, Portugal"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8512-965X","authenticated-orcid":false,"given":"Antonio","family":"G. Ravelo-Garc\u00eda","sequence":"additional","affiliation":[{"name":"Institute for Technological Development and Innovation in Communications, Universidad de Las Palmas de Gran Canaria, 35001 Las Palmas, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7334-3993","authenticated-orcid":false,"given":"Fernando","family":"Morgado-Dias","sequence":"additional","affiliation":[{"name":"Faculdade de Ci\u00eancias Exatas e da Engenharia, Universidade da Madeira, 9000-082 Funchal, Portugal"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,11,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1387","DOI":"10.1378\/chest.14-0970","article-title":"International Classification of Sleep Disorders-Third Edition (ICSD-3)","volume":"146","author":"Sateia","year":"2014","journal-title":"Chest"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Zhang, J., Zhang, Q., Wang, Y., and Qiu, C. (2013, January 8\u201311). A Real-time auto-adjustable smart pillow system for sleep apnea detection and treatment. Proceedings of the 12th International Conference on Information Processing in Sensor Networks (IPSN), Philadelphia, PA, USA.","DOI":"10.1145\/2461381.2461405"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1230","DOI":"10.1056\/NEJM199304293281704","article-title":"The occurrence of sleep-disordered breathing among middle-aged adults","volume":"328","author":"Young","year":"1993","journal-title":"N. Engl. J. Med."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"705","DOI":"10.1093\/sleep\/20.9.705","article-title":"Estimation of the clinically diagnosed proportion of sleep apnea syndrome in middle-aged men and women","volume":"20","author":"Young","year":"1997","journal-title":"Sleep"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"963","DOI":"10.1378\/chest.107.4.963","article-title":"Snoring, Apneic Episodes, and Nocturnal Hypoxemia Among Children 6 Months to 6 Years Old","volume":"107","author":"Gislason","year":"1995","journal-title":"Chest"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1400","DOI":"10.1378\/chest.124.4.1400","article-title":"The relationship between congestive heart failure, sleep apnea, and mortality in older men","volume":"124","author":"DuHamel","year":"2003","journal-title":"Chest"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1151","DOI":"10.1210\/jcem.85.3.6484","article-title":"Sleep Apnea and Daytime Sleepiness and Fatigue: Relation to Visceral Obesity, Insulin Resistance, and Hypercytokinemia","volume":"85","author":"Vgontzas","year":"2000","journal-title":"J. Clin. Endocrinol. Metab."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"14","DOI":"10.2337\/diaspect.29.1.14","article-title":"Sleep Apnea in Type 2 Diabetes","volume":"29","author":"Doumit","year":"2016","journal-title":"Diabetes Spectr."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"416","DOI":"10.1109\/TITB.2010.2087386","article-title":"Apnea MedAssist: Real-time Sleep Apnea Monitor Using Single-Lead ECG","volume":"15","author":"Bsoul","year":"2011","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"967","DOI":"10.1088\/0967-3334\/25\/4\/015","article-title":"Automated Detection of Obstructive Sleep Apnoea at Different Time Scales using the Electrocardiogram","volume":"25","author":"Penzel","year":"2004","journal-title":"Physiol. Meas."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1412","DOI":"10.1109\/10.966600","article-title":"Computer-Assisted Sleep Staging","volume":"48","author":"Agarwal","year":"2001","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1093\/sleep\/29.3.299","article-title":"The Economic Cost of Sleep Disorders","volume":"29","author":"Hillman","year":"2006","journal-title":"Sleep"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1007\/s00408-007-9055-5","article-title":"The Economic Impact of Obstructive Sleep Apnea","volume":"186","author":"Alghanim","year":"2008","journal-title":"Lung"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1057","DOI":"10.1109\/TITB.2009.2031639","article-title":"Automated Scoring of Obstructive Sleep Apnea and Hypopnea Events Using Short-Term Electrocardiogram Recordings","volume":"13","author":"Khandoker","year":"2009","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1088\/0967-3334\/31\/3\/001","article-title":"Automatic screening of obstructive sleep apnea from the ECG based on empirical mode decomposition and wavelet analysis","volume":"31","author":"Mendez","year":"2010","journal-title":"Physiol. Meas."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Mostafa, S.S., Morgado-Dias, F., and Ravelo-Garc\u00eda, A.G. (2018). Comparison of SFS and mRMR for oximetry feature selection in obstructive sleep apnea detection. Neural Comput. Appl., 1\u201321.","DOI":"10.1007\/s00521-018-3455-8"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1109\/TITB.2012.2185809","article-title":"Automated Recognition of Obstructive Sleep Apnea Syndrome Using Support Vector Machine Classifier","volume":"16","author":"Sahakian","year":"2012","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"7778","DOI":"10.1016\/j.eswa.2008.11.043","article-title":"Fuzzy reasoning used to detect apneic events in the sleep apnea-hypopnea syndrome","volume":"36","year":"2009","journal-title":"Expert Syst. Appl."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"187","DOI":"10.5391\/IJFIS.2017.17.3.187","article-title":"Design of a Fast Learning Classifier for Sleep Apnea Database based on Fuzzy SVM","volume":"17","author":"Lee","year":"2017","journal-title":"Int. J. Fuzzy Log. Intell. Syst."},{"key":"ref_20","first-page":"7","article-title":"A Neural Network System for Detection of Obstructive Sleep Apnea Through SpO2 Signal Features","volume":"3","author":"Almazaydeh","year":"2012","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Mostafa, S.S., Carvalho, J.P., Morgado-Dias, F., and Ravelo-Garc\u00eda, A. (2017, January 26\u201328). Optimization of sleep apnea detection using SpO2 and ANN. Proceedings of the XXVI International Conference on Information, Communication and Automation Technologies (ICAT), Sarajevo, Bosnia-Herzegovina.","DOI":"10.1109\/ICAT.2017.8171609"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2932","DOI":"10.3390\/e17052932","article-title":"Oxygen Saturation and RR Intervals Feature Selection for Sleep Apnea Detection","volume":"17","author":"Kraemer","year":"2015","journal-title":"Entropy"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1109\/TSMC.1974.5408535","article-title":"The Best Two Independent Measurements Are Not the Two Best","volume":"SMC-4","author":"Cover","year":"1974","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2838","DOI":"10.1109\/TBME.2009.2029563","article-title":"Sleep Apnea Screening by Autoregressive Models from a Single ECG Lead","volume":"56","author":"Mendez","year":"2009","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Isa, S.M., Fanany, M.I., Jatmiko, W., and Arymurthy, A.M. (2011, January 10\u201312). Sleep apnea detection from ECG signal: Analysis on optimal features, principal components, and nonlinearity. Proceedings of the IEEE 5th International Conference on Bioinformatics and Biomedical Engineering, Wuhan, China.","DOI":"10.1109\/icbbe.2011.5780285"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.smrv.2018.02.004","article-title":"Devices for Home Detection of Obstructive Sleep Apnea: A Review","volume":"41","author":"Mostafa","year":"2018","journal-title":"Sleep Med. Rev."},{"key":"ref_27","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","year":"2019","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_28","first-page":"VE01","article-title":"A Review on Detection and Treatment Methods of Sleep Apnea","volume":"11","author":"Jayaraj","year":"2017","journal-title":"J. Clin. Diagn. Res."},{"key":"ref_29","unstructured":"Penzel, T., Moody, G., Mark, R., Goldberger, A., and Peter, J. (2000, January 24\u201327). The apnea-ECG database. Proceedings of the Computers in Cardiology, Cambridge, MA, USA."},{"key":"ref_30","unstructured":"(2019, February 20). PhysioNet. Available online: www.physionet.org."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"e215","DOI":"10.1161\/01.CIR.101.23.e215","article-title":"PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals","volume":"101","author":"Goldberger","year":"2000","journal-title":"Circulation"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.neucom.2018.03.011","article-title":"A method to detect sleep apnea based on deep neural network and hidden Markov model using single-lead ECG signal","volume":"294","author":"Li","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Pathinarupothi, R.K., Rangan, E.S., Gopalakrishnan, E.A., Vinaykumar, R., and Soman, K.P. (2017, January 23\u201326). Single sensor techniques for sleep apnea diagnosis using deep learning. Proceedings of the IEEE International Conference on Healthcare Informatics (ICHI), Park City, UT, USA.","DOI":"10.1109\/ICHI.2017.37"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Pathinarupothi, R.K., Vinaykumar, R., Rangan, E., Gopalakrishnan, E., and Soman, K.P. (2017, January 16\u201319). Instantaneous heart rate as a robust feature for sleep apnea severity detection using deep learning. Proceedings of the IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), Orlando, FL, USA.","DOI":"10.1109\/BHI.2017.7897263"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Novak, D., Mucha, K., and Al-Ani, T. (2008, January 20\u201324). Long Short-Term Memory for apnea detection based on heart rate variability. Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Vancouver, BC, Canada.","DOI":"10.1109\/IEMBS.2008.4650394"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"De Falco, I., De Pietro, G., Sannino, G., Scafuri, U., Tarantino, E., Della Cioppa, A., and Trunfio, G.A. (2018, January 25\u201328). Deep neural network hyper-parameter setting for classification of obstructive sleep apnea episodes. Proceedings of the 2018 IEEE Symposium on Computers and Communications (ISCC), Natal, Brazil.","DOI":"10.1109\/ISCC.2018.8538572"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1007\/s13534-017-0055-y","article-title":"Obstructive sleep apnoea detection using convolutional neural network based deep learning framework","volume":"8","author":"Dey","year":"2018","journal-title":"Biomed. Eng. Lett."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Banluesombatkul, N., Rakthanmanon, T., and Wilaiprasitporn, T. (2018, January 28\u201331). Single Channel ECG for Obstructive Sleep Apnea Severity Detection using a Deep Learning Approach. Proceedings of the TENCON 2018\u20142018 IEEE Region 10 Conference, Jeju, Korea.","DOI":"10.1109\/TENCON.2018.8650429"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1151","DOI":"10.5665\/sleep.5774","article-title":"Scaling Up Scientific Discovery in Sleep Medicine: The National Sleep Research Resource","volume":"39","author":"Dean","year":"2016","journal-title":"Sleep"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"557","DOI":"10.1016\/j.cct.2005.05.005","article-title":"Overview of recruitment for the osteoporotic fractures in men study (MrOS)","volume":"26","author":"Blank","year":"2005","journal-title":"Contemp. Clin. Trials"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1016\/j.cct.2005.05.006","article-title":"Design and baseline characteristics of the osteoporotic fractures in men (MrOS) study--a large observational study of the determinants of fracture in older men","volume":"26","author":"Orwoll","year":"2005","journal-title":"Contemp. Clin. Trials"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"2217","DOI":"10.1111\/j.1532-5415.2011.03731.x","article-title":"Associations between sleep architecture and sleep-disordered breathing and cognition in older community-dwelling men: The Osteoporotic Fractures in Men Sleep Study","volume":"59","author":"Blackwell","year":"2011","journal-title":"J. Am. Geriatr. Soc."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"065003","DOI":"10.1088\/1361-6579\/aac7b7","article-title":"Multiclass classification of obstructive sleep apnea\/hypopnea based on a convolutional neural network from a single-lead electrocardiogram","volume":"39","author":"Urtnasan","year":"2018","journal-title":"Physiol. Meas."},{"key":"ref_44","unstructured":"Berry, B.R., Brooks, R., Gamaldo, E.C., Harding, M.S., Marcus, C., and Vaughn, B. (2012). AASM Manual for the Scoring of Sleep and Associated Events. Rules, Terminology and Technical Specifications, AASM."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Urtnasan, E., Park, J.U., and Lee, K.J. (2018). Automatic detection of sleep-disordered breathing events using recurrent neural networks from an electrocardiogram signal. Neural Comput. Appl.","DOI":"10.1007\/s00521-018-3833-2"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1007\/s10916-018-0963-0","article-title":"Automated Detection of Obstructive Sleep Apnea Events from a Single-Lead Electrocardiogram Using a Convolutional Neural Network","volume":"42","author":"Urtnasan","year":"2018","journal-title":"J. Med. Syst."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Mostafa, S.S., Mendon\u00e7a, F., Morgado-Dias, F., and Ravelo-Garc\u00eda, A. (2017, January 20\u201323). SpO2 based sleep apnea detection using deep learning. Proceedings of the 2017 IEEE 21st International Conference on Intelligent Engineering Systems (INES), Larnaca, Cyprus.","DOI":"10.1109\/INES.2017.8118534"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Cen, L., Yu, Z.L., Kluge, T., and Ser, W. (2018, January 18\u201321). Automatic system for obstructive sleep apnea events detection using convolutional neural network. Proceedings of the 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Honolulu, HI, USA.","DOI":"10.1109\/EMBC.2018.8513363"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1643","DOI":"10.1093\/jamia\/ocy131","article-title":"Expert-level sleep scoring with deep neural networks","volume":"25","author":"Biswal","year":"2018","journal-title":"J. Am. Med. Informatics Assoc."},{"key":"ref_50","unstructured":"(2019, January 11). Sleep Heart Health Study. Available online: https:\/\/sleepdata.org\/datasets\/shhs."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.compbiomed.2018.06.028","article-title":"Real-time apnea-hypopnea event detection during sleep by convolutional neural networks","volume":"100","author":"Choi","year":"2018","journal-title":"Comput. Biol. Med."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1186\/s12938-018-0448-x","article-title":"Detection of sleep disordered breathing severity using acoustic biomarker and machine learning techniques","volume":"17","author":"Kim","year":"2018","journal-title":"Biomed. Eng. Online"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Haidar, R., McCloskey, S., Koprinska, I., and Jeffries, B. (2018, January 8\u201313). Convolutional neural networks on multiple respiratory channels to detect hypopnea and obstructive apnea events. Proceedings of the 2018 International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro, Brazil.","DOI":"10.1109\/IJCNN.2018.8489248"},{"key":"ref_54","first-page":"1077","article-title":"The Sleep Heart Health Study: Design, rationale, and methods","volume":"20","author":"Quan","year":"1997","journal-title":"Sleep"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Van Steenkiste, T., Groenendaal, W., Deschrijver, D., and Dhaene, T. (2018). Automated Sleep Apnea Detection in Raw Respiratory Signals using Long Short-Term Memory Neural Networks. IEEE J. Biomed. Heal. Informatics.","DOI":"10.1109\/JBHI.2018.2886064"},{"key":"ref_56","unstructured":"(2019, February 12). Technical Notes on SHHS1. Available online: https:\/\/www.sleepdata.org\/datasets\/shhs\/pages\/08-equipment-shhs1.md."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Lakhan, P., Ditthapron, A., Banluesombatkul, N., and Wilaiprasitporn, T. (2018, January 28\u201331). Deep neural networks with weighted averaged overnight airflow features for sleep apnea-hypopnea severity classification. Proceedings of the TENCON, IEEE Region 10 International Conference, Jeju, Korea.","DOI":"10.1109\/TENCON.2018.8650491"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Lee-Chiong, T.L. (2008). Sleep Medicine: Essentials and Review, Oxford University Press.","DOI":"10.1093\/oso\/9780195306590.001.0001"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"McCloskey, S., Haidar, R., Koprinska, I., and Jeffries, B. (2018, January 3\u20136). Detecting hypopnea and obstructive apnea events using convolutional neural networks on wavelet spectrograms of nasal airflow. Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), Melbourne, Australia.","DOI":"10.1007\/978-3-319-93034-3_29"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Haidar, R., Koprinska, I., and Jeffries, B. (2017, January 14\u201318). Sleep apnea event detection from nasal airflow using convolutional neural networks. Proceedings of the International Conference on Neural Information Processing (ICONIP), Guangzhou, China.","DOI":"10.1007\/978-3-319-70139-4_83"},{"key":"ref_61","unstructured":"(2019, February 25). St. Vincent\u2019s University Hospital\/University College Dublin Sleep Apnea Database. Available online: https:\/\/physionet.org\/pn3\/ucddb\/."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Cheng, M., Sori, W.J., Jiang, F., Khan, A., and Liu, S. (2017, January 21\u201324). Recurrent neural network based classification of ECG signal features for obstruction of sleep apnea detection. Proceedings of the 2017 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC), Guangzhou, China.","DOI":"10.1109\/CSE-EUC.2017.220"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1109\/TASSP.1979.1163209","article-title":"Suppression of acoustic noise in speech using spectral subtraction","volume":"27","author":"Boll","year":"1979","journal-title":"IEEE Trans. Acoust."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1186\/s12938-016-0306-7","article-title":"Exploiting temporal and nonstationary features in breathing sound analysis for multiple obstructive sleep apnea severity classification","volume":"16","author":"Kim","year":"2017","journal-title":"Biomed. Eng. Online"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Van Steenkiste, T., Groenendaal, W., Ruyssinck, J., Dreesen, P., Klerkx, S., Smeets, C., de Francisco, R., Deschrijver, D., and Dhaene, T. (2018, January 18\u201321). Systematic comparison of respiratory signals for the automated detection of sleep apnea. Proceedings of the 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Honolulu, HI, USA.","DOI":"10.1109\/EMBC.2018.8512307"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Tian, J.Y., and Liu, J.Q. (2006, January 17\u201318). Apnea detection based on time delay neural network. Proceedings of the 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, Shanghai, China.","DOI":"10.1109\/IEMBS.2005.1616994"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1109\/TBME.1985.325532","article-title":"A Real-Time QRS Detection Algorithm","volume":"BME-32","author":"Pan","year":"1985","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1109\/TASE.2014.2345667","article-title":"An Automatic Screening Approach for Obstructive Sleep Apnea Diagnosis Based on Single-Lead Electrocardiogram","volume":"12","author":"Chen","year":"2015","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"ref_69","unstructured":"(2018, December 18). Software for Viewing, Analyzing, and Creating Recordings of Physiologic Signals. Available online: https:\/\/physionet.org\/physiotools\/wfdb.shtml."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.cmpb.2004.03.004","article-title":"Software for advanced HRV analysis","volume":"76","author":"Niskanen","year":"2004","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_71","unstructured":"Haykin, S. (2001). Neural Networks: A Comprehnsive Foundation, Pearson Education. [2nd ed.]."},{"key":"ref_72","unstructured":"Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning, MIT Press."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"3311","DOI":"10.1016\/S0042-6989(97)00169-7","article-title":"Sparse coding with an overcomplete basis set: A strategy employed by V1?","volume":"37","author":"Olshausen","year":"1997","journal-title":"Vision Res."},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Salakhutdinov, R., and Murray, I. (2008, January 5\u20139). On the quantitative analysis of deep belief networks. Proceedings of the 25th International Conference on Machine learning\u2014ICML \u201908, Helsinki, Finland.","DOI":"10.1145\/1390156.1390266"},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Ren, J.S.J., and Xu, L. (2015, January 25\u201329). On vectorization of deep convolutional neural networks for vision tasks. Proceedings of the 29th AAAI Conference on Artificial Intelligence.","DOI":"10.1609\/aaai.v29i1.9488"},{"key":"ref_76","first-page":"1","article-title":"Understanding Convolutional Neural Networks","volume":"2014","author":"Stutz","year":"2016","journal-title":"Nips"},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Nagi, J., and Ducatelle, F. (2011, January 16\u201318). Max-pooling convolutional neural networks for vision-based hand gesture recognition. Proceedings of the IEEE International Conference on Signal and Image Processing Applications (ICSIPA), Kuala Lumpur, Malaysia.","DOI":"10.1109\/ICSIPA.2011.6144164"},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Baptista, D., Mostafa, S., Pereira, L., Sousa, L., Morgado-Dias, F., Baptista, D., Mostafa, S.S., Pereira, L., Sousa, L., and Morgado-Dias, F. (2018). Implementation Strategy of Convolution Neural Networks on Field Programmable Gate Arrays for Appliance Classification Using the Voltage and Current (V-I) Trajectory. Energies, 11.","DOI":"10.3390\/en11092460"},{"key":"ref_79","unstructured":"Memisevic, R., Zach, C., Hinton, G.E., and Pollefeys, M. (2010, January 6\u201311). Gated softmax classification. Proceedings of the Advances in Neural Information Processing Systems 23 (NIPS 2010), Vancouver, BC, Canada."},{"key":"ref_80","unstructured":"Ioffe, S., and Szegedy, C. (2015, January 6\u201311). Batch normalization: Accelerating deep network training by reducing internal covariate shift. Proceedings of the ICML\u201915 32nd International Conference on International Conference on Machine Learning, Lille, France."},{"key":"ref_81","first-page":"1929","article-title":"Dropout: A Simple Way to Prevent Neural Networks from Overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"ImageNet Classification with Deep Convolutional Neural Networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. ACM"},{"key":"ref_83","unstructured":"Gao, Y., and Glowacka, D. (2016, January 16\u201318). Deep Gate Recurrent Neural Network. Proceedings of the Asian Conference on Machine Learning, Hamilton, New Zealand."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"616","DOI":"10.1109\/TII.2016.2601521","article-title":"Understanding Subtitles by Character-Level Sequence-to-Sequence Learning","volume":"13","author":"Zhang","year":"2017","journal-title":"IEEE Trans. Ind. Informatics"},{"key":"ref_86","unstructured":"Chung, J., Gulcehre, C., Cho, K., and Bengio, Y. (2014). Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling. arXiv."},{"key":"ref_87","first-page":"48","article-title":"Part 1: Simple Definition and Calculation of Accuracy, Sensitivity and Specificity","volume":"3","author":"Baratloo","year":"2015","journal-title":"Emergency (Iran)"},{"key":"ref_88","first-page":"1","article-title":"ROC Graphs: Notes and Practical Considerations for Data Mining Researchers. Hp L-2003-4","volume":"31","author":"Fawcett","year":"2004","journal-title":"Mach. Learn."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1007\/978-3-030-04663-7_4","article-title":"Learning from imbalanced data","volume":"807","author":"Vluymans","year":"2019","journal-title":"Stud. Comput. Intell."},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Wallace, B.C., Small, K., Brodley, C.E., and Trikalinos, T.A. (2011, January 11\u201314). Class imbalance, redux. Proceedings of the IEEE 11th International Conference on Data Mining, Vancouver, BC, Canada.","DOI":"10.1109\/ICDM.2011.33"},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1186\/s40537-019-0192-5","article-title":"Survey on deep learning with class imbalance","volume":"6","author":"Johnson","year":"2019","journal-title":"J. Big Data"},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"1532","DOI":"10.1109\/TBME.2015.2498199","article-title":"An Obstructive Sleep Apnea Detection Approach Using a Discriminative Hidden Markov Model from ECG Signals","volume":"63","author":"Song","year":"2016","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"1285","DOI":"10.1109\/JBHI.2013.2292928","article-title":"An Online Sleep Apnea Detection Method Based on Recurrence Quantification Analysis","volume":"18","author":"Nguyen","year":"2014","journal-title":"IEEE J. Biomed. 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