{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T03:46:26Z","timestamp":1792035986069,"version":"4.4.0"},"reference-count":68,"publisher":"PeerJ","license":[{"start":{"date-parts":[[2023,9,29]],"date-time":"2023-09-29T00:00:00Z","timestamp":1695945600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:p>Sleep apnea is defined as a breathing disorder that affects sleep. Early detection of sleep apnea helps doctors to take intervention for patients to prevent sleep apnea. Manually making this determination is a time-consuming and subjectivity problem. Therefore, many different methods based on polysomnography (PSG) have been proposed and applied to detect this disorder. In this study, a unique two-layer method is proposed, in which there are four different deep learning models in the deep neural network (DNN), gated recurrent unit (GRU), recurrent neural network (RNN), RNN-based-long term short term memory (LSTM) architecture in the first layer, and a machine learning-based meta-learner (decision-layer) in the second layer. The strategy of making a preliminary decision in the first layer and verifying\/correcting the results in the second layer is adopted. In the training of this architecture, a vector consisting of 23 features consisting of snore, oxygen saturation, arousal and sleep score data is used together with PSG data. A dataset consisting of 50 patients, both children and adults, is prepared. A number of pre-processing and under-sampling applications have been made to eliminate the problem of unbalanced classes. Proposed method has an accuracy of 95.74% and 99.4% in accuracy of apnea detection (apnea, hypopnea and normal) and apnea types detection (central, mixed and obstructive), respectively. Experimental results demonstrate that patient-independent consistent results can be produced with high accuracy. This robust model can be considered as a system that will help in the decisions of sleep clinics where it is expected to detect sleep disorders in detail with high performance.<\/jats:p>","DOI":"10.7717\/peerj-cs.1554","type":"journal-article","created":{"date-parts":[[2023,9,29]],"date-time":"2023-09-29T09:06:22Z","timestamp":1695978382000},"page":"e1554","source":"Crossref","is-referenced-by-count":7,"title":["Sleep disorder and apnea events detection framework with high performance using two-tier learning model design"],"prefix":"10.7717","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3028-0416","authenticated-orcid":true,"given":"Recep Sinan","family":"Arslan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"4443","published-online":{"date-parts":[[2023,9,29]]},"reference":[{"key":"10.7717\/peerj-cs.1554\/ref-1","doi-asserted-by":"publisher","first-page":"111","DOI":"10.12659\/msm.883776","article-title":"Neck circumference, metabolic syndrome and obstructive sleep apnea syndrome; evaluation of possible linkage","volume":"19","author":"Ahbab","year":"2013","journal-title":"Medical Science Monitor"},{"issue":"1","key":"10.7717\/peerj-cs.1554\/ref-2","doi-asserted-by":"publisher","first-page":"1006","DOI":"10.1038\/s41598-020-62223-4","article-title":"A machine learning-based test for adult sleep apnoea screening at home using oximetry and Airflow","volume":"10","author":"\u00c1lvarez","year":"2020","journal-title":"Scientific Reports"},{"issue":"10","key":"10.7717\/peerj-cs.1554\/ref-3","doi-asserted-by":"publisher","first-page":"7495","DOI":"10.1007\/s00521-022-08037-z","article-title":"Sensitive deep learning application on sleep stage scoring by using all PSG data","volume":"35","author":"Arslan","year":"2022","journal-title":"Neural Computing and Applications"},{"issue":"1","key":"10.7717\/peerj-cs.1554\/ref-4","doi-asserted-by":"publisher","first-page":"105653","DOI":"10.1016\/j.compbiomed.2022.105653","article-title":"Automated sleep scoring system using multi-channel data and machine learning","volume":"146","author":"Arslan","year":"2022","journal-title":"Computers in Biology and Medicine"},{"key":"10.7717\/peerj-cs.1554\/ref-5","doi-asserted-by":"publisher","DOI":"10.1109\/tencon.2018.8650429","article-title":"Single channel ECG for obstructive sleep apnea severity detection using a deep learning approach","author":"Banluesombatkul","year":"2018"},{"issue":"4","key":"10.7717\/peerj-cs.1554\/ref-6","doi-asserted-by":"publisher","first-page":"1491","DOI":"10.3390\/s21041491","article-title":"Wavelet analysis of overnight airflow to detect obstructive sleep apnea in children","volume":"21","author":"Barroso-Garc\u00eda","year":"2021","journal-title":"Sensors"},{"issue":"8","key":"10.7717\/peerj-cs.1554\/ref-7","doi-asserted-by":"publisher","first-page":"687","DOI":"10.1016\/S2213-2600(19)30198-5","article-title":"Estimation of the global prevalence and burden of obstructive sleep apnoea: a literature-based analysis","volume":"7","author":"Benjafield","year":"2019","journal-title":"The Lancet Respiratory Medicine"},{"issue":"3","key":"10.7717\/peerj-cs.1554\/ref-8","doi-asserted-by":"publisher","first-page":"608","DOI":"10.1164\/ajrccm.163.3.9911064","article-title":"Prevalence of sleep-disordered breathing in women","volume":"163","author":"B\u0131xler","year":"2001","journal-title":"American Journal of Respiratory and Critical Care Medicine"},{"issue":"15","key":"10.7717\/peerj-cs.1554\/ref-9","doi-asserted-by":"publisher","first-page":"4157","DOI":"10.3390\/s20154157","article-title":"A sleep apnea detection system based on a one-dimensional deep convolution neural network model using single-lead electrocardiogram","volume":"20","author":"Chang","year":"2020","journal-title":"Sensors"},{"key":"10.7717\/peerj-cs.1554\/ref-10","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/w14-4012","article-title":"On the properties of neural machine translation: encoder-decoder approaches","author":"Cho","year":"2014"},{"issue":"11","key":"10.7717\/peerj-cs.1554\/ref-11","doi-asserted-by":"publisher","first-page":"8933","DOI":"10.1007\/s00521-022-06919-w","article-title":"Hybridization of soft-computing algorithms with Neural Network for prediction obstructive sleep apnea using biomedical sensor measurements","volume":"34","author":"Chyad","year":"2022","journal-title":"Neural Computing and Applications"},{"issue":"7553","key":"10.7717\/peerj-cs.1554\/ref-12","doi-asserted-by":"publisher","first-page":"377","DOI":"10.1016\/j.future.2019.01.049","article-title":"Evolution-based configuration optimization of a deep neural network for the classification of obstructive sleep apnea episodes","volume":"98","author":"De Falco","year":"2019","journal-title":"Future Generation Computer Systems"},{"issue":"1","key":"10.7717\/peerj-cs.1554\/ref-13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2021\/7152576","article-title":"Development of an IOT-based sleep apnea monitoring system for Healthcare Applications","volume":"2021","author":"Dhruba","year":"2021","journal-title":"Computational and Mathematical Methods in Medicine"},{"issue":"19","key":"10.7717\/peerj-cs.1554\/ref-14","doi-asserted-by":"publisher","first-page":"1840","DOI":"10.1161\/circulationaha.117.029400","article-title":"Sleep apnea and cardiovascular disease","volume":"136","author":"Drager","year":"2017","journal-title":"Circulation"},{"issue":"4","key":"10.7717\/peerj-cs.1554\/ref-15","doi-asserted-by":"publisher","first-page":"656","DOI":"10.1513\/annalsats.202001-064oc","article-title":"A novel model to estimate key obstructive sleep apnea endotypes from standard polysomnography and clinical data and their contribution to obstructive sleep apnea severity","volume":"18","author":"Dutta","year":"2021","journal-title":"Annals of the American Thoracic Society"},{"key":"10.7717\/peerj-cs.1554\/ref-16","doi-asserted-by":"publisher","first-page":"105001","DOI":"10.1016\/j.cmpb.2019.105001","article-title":"Deep learning approaches for automatic detection of sleep apnea events from an electrocardiogram","volume":"180","author":"Erdenebayar","year":"2019","journal-title":"Computer Methods and Programs in Biomedicine"},{"issue":"3","key":"10.7717\/peerj-cs.1554\/ref-17","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1590\/2177-6709.24.3.099-109.sar","article-title":"Obstructive sleep apnea in adults","volume":"24","author":"Faber","year":"2019","journal-title":"Dental Press Journal of Orthodontics"},{"issue":"12","key":"10.7717\/peerj-cs.1554\/ref-18","doi-asserted-by":"publisher","first-page":"1741","DOI":"10.1161\/circresaha.118.310783","article-title":"Sleep apnea and cardiovascular disease","volume":"122","author":"Floras","year":"2018","journal-title":"Circulation Research"},{"issue":"7","key":"10.7717\/peerj-cs.1554\/ref-19","doi-asserted-by":"publisher","first-page":"1616","DOI":"10.3390\/biomedicines10071616","article-title":"A systematic review of sleep-wake disorder diagnostic criteria reliability studies","volume":"10","author":"Gauld","year":"2022","journal-title":"Biomedicines"},{"issue":"2","key":"10.7717\/peerj-cs.1554\/ref-20","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1007\/s11325-015-1294-8","article-title":"Somatic arousal and sleepiness\/fatigue among patients with sleep-disordered breathing","volume":"20","author":"Gold","year":"2016","journal-title":"Sleep and Breathing"},{"issue":"2","key":"10.7717\/peerj-cs.1554\/ref-21","doi-asserted-by":"publisher","first-page":"882","DOI":"10.1109\/jbhi.2018.2823384","article-title":"Evaluation of machine-learning approaches to estimate sleep apnea severity from at-home oximetry recordings","volume":"23","author":"Gutierrez-Tobal","year":"2019","journal-title":"IEEE Journal of Biomedical and Health Informatics"},{"key":"10.7717\/peerj-cs.1554\/ref-22","article-title":"Deep learning to detect obstructive sleep apnea events from breathing. University of Oslo, Master Thesis","author":"Hamnvik","year":"2021"},{"issue":"12","key":"10.7717\/peerj-cs.1554\/ref-23","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1016\/j.neucom.2016.12.062","article-title":"An expert system for automated identification of obstructive sleep apnea from single-lead ECG using random under sampling boosting","volume":"235","author":"Hassan","year":"2017","journal-title":"Neurocomputing"},{"key":"10.7717\/peerj-cs.1554\/ref-24","doi-asserted-by":"publisher","DOI":"10.1101\/2021.04.19.21255733","article-title":"Developing and comparing machine learning models to detect sleep apnoea using single-lead electrocardiogram (ECG) monitoring","author":"Hedman","year":"2021","journal-title":"MedRxiv"},{"key":"10.7717\/peerj-cs.1554\/ref-25","doi-asserted-by":"publisher","DOI":"10.1109\/memea52024.2021.9478677","article-title":"Unobtrusively detecting apnea and hypopnea events via a hydraulic bed sensor","author":"Heise","year":"2021"},{"issue":"1","key":"10.7717\/peerj-cs.1554\/ref-26","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1007\/s11325-018-1672-0","article-title":"Sleep apnea detection: accuracy of using automated ECG analysis compared to manually scored polysomnography (apnea hypopnea index)","volume":"23","author":"Hilmisson","year":"2018","journal-title":"Sleep and Breathing"},{"issue":"8","key":"10.7717\/peerj-cs.1554\/ref-27","doi-asserted-by":"publisher","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 Computation"},{"issue":"7","key":"10.7717\/peerj-cs.1554\/ref-28","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1093\/sleep\/zsz295","article-title":"Support Vector Machine Prediction of obstructive sleep apnea in a large-scale Chinese clinical sample","volume":"43","author":"Huang","year":"2020","journal-title":"Sleep"},{"key":"10.7717\/peerj-cs.1554\/ref-29","doi-asserted-by":"publisher","DOI":"10.1109\/elnano50318.2020.9088749","article-title":"Identifying episodes of sleep apnea in ECG by Machine Learning Methods","author":"Ivanko","year":"2020"},{"issue":"4","key":"10.7717\/peerj-cs.1554\/ref-30","doi-asserted-by":"publisher","first-page":"1821","DOI":"10.1007\/s11325-020-02249-0","article-title":"Screening of sleep apnea based on heart rate variability and long short-term memory","volume":"25","author":"Iwasaki","year":"2021","journal-title":"Sleep and Breathing"},{"issue":"7","key":"10.7717\/peerj-cs.1554\/ref-31","doi-asserted-by":"publisher","first-page":"841","DOI":"10.1016\/j.jacc.2016.11.069","article-title":"Sleep apnea","volume":"69","author":"Javaheri","year":"2017","journal-title":"Journal of the American College of Cardiology"},{"issue":"1","key":"10.7717\/peerj-cs.1554\/ref-32","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1017\/s0022215114003119","article-title":"Nasopharyngeal airway stenting devices for obstructive sleep apnoea: a systematic review and meta-analysis","volume":"129","author":"Kumar","year":"2014","journal-title":"The Journal of Laryngology & Otology"},{"issue":"5","key":"10.7717\/peerj-cs.1554\/ref-33","doi-asserted-by":"publisher","first-page":"1496","DOI":"10.1109\/TBME.2020.3028041","article-title":"Detection and classification of sleep apnea and hypopnea using PPG and SPO$_2$ signals","volume":"68","author":"Lazazzera","year":"2021","journal-title":"IEEE Transactions on Biomedical Engineering"},{"issue":"2","key":"10.7717\/peerj-cs.1554\/ref-34","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1007\/s00408-021-00426-w","article-title":"Evaluation and management of adults with obstructive sleep apnea syndrome","volume":"199","author":"Lee","year":"2021","journal-title":"Lung"},{"issue":"4","key":"10.7717\/peerj-cs.1554\/ref-35","doi-asserted-by":"publisher","first-page":"2065","DOI":"10.1007\/s11325-021-02302-6","article-title":"A model for obstructive sleep apnea detection using a multi-layer feed-forward neural network based on electrocardiogram, pulse oxygen saturation, and body mass index","volume":"25","author":"Li","year":"2021","journal-title":"Sleep and Breathing"},{"issue":"2","key":"10.7717\/peerj-cs.1554\/ref-36","doi-asserted-by":"publisher","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"},{"issue":"8","key":"10.7717\/peerj-cs.1554\/ref-37","doi-asserted-by":"publisher","first-page":"103765","DOI":"10.1016\/j.bspc.2022.103765","article-title":"Contactless sleep apnea detection in snoring signals using hybrid deep neural networks targeted for embedded hardware platform with real-time applications","volume":"77","author":"Lin","year":"2022","journal-title":"Biomedical Signal Processing and Control"},{"issue":"6","key":"10.7717\/peerj-cs.1554\/ref-38","doi-asserted-by":"publisher","first-page":"532","DOI":"10.1001\/jamacardio.2018.0095","article-title":"Associations of obstructive sleep apnea with atrial fibrillation and continuous positive airway pressure treatment","volume":"3","author":"Linz","year":"2018","journal-title":"JAMA Cardiology"},{"issue":"7","key":"10.7717\/peerj-cs.1554\/ref-39","doi-asserted-by":"publisher","first-page":"736","DOI":"10.1093\/sleep\/zsab030","article-title":"Metrics of sleep apnea severity: beyond the apnea-hypopnea index","volume":"44","author":"Malhotra","year":"2021","journal-title":"Sleep"},{"issue":"1","key":"10.7717\/peerj-cs.1554\/ref-40","doi-asserted-by":"publisher","first-page":"298","DOI":"10.1177\/1460458218824725","article-title":"Application of machine learning to predict obstructive sleep apnea syndrome severity","volume":"26","author":"Mencar","year":"2019","journal-title":"Health Informatics Journal"},{"issue":"2","key":"10.7717\/peerj-cs.1554\/ref-41","doi-asserted-by":"publisher","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 Journal of Biomedical and Health Informatics"},{"issue":"6","key":"10.7717\/peerj-cs.1554\/ref-42","doi-asserted-by":"publisher","first-page":"2094","DOI":"10.1109\/TBME.2021.3136753","article-title":"Automated scoring of respiratory events in sleep with a single effort belt and Deep Neural Networks","volume":"69","author":"Nassi","year":"2022","journal-title":"IEEE Transactions on Biomedical Engineering"},{"key":"10.7717\/peerj-cs.1554\/ref-43","article-title":"Using pure. Eindhoven University of Technology. (n.d.)","author":"Papini","year":"2022"},{"issue":"Supplement_1","key":"10.7717\/peerj-cs.1554\/ref-44","doi-asserted-by":"publisher","first-page":"A208","DOI":"10.1093\/sleep\/zsz067.518","article-title":"0520 treatment of adult obstructive sleep apnea with positive airway pressure: an American Academy of Sleep Medicine Clinical Practice guideline","volume":"42","author":"Patil","year":"2019","journal-title":"Sleep"},{"issue":"7","key":"10.7717\/peerj-cs.1554\/ref-45","doi-asserted-by":"publisher","first-page":"914","DOI":"10.3390\/healthcare9070914","article-title":"A survey on recent advances in machine learning based sleep apnea detection systems","volume":"9","author":"Ramachandran","year":"2021","journal-title":"Healthcare"},{"key":"10.7717\/peerj-cs.1554\/ref-46","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3412686","article-title":"An extensive investigation of machine learning techniques for sleep apnea screening","author":"Rodrigues","year":"2020"},{"issue":"11","key":"10.7717\/peerj-cs.1554\/ref-47","doi-asserted-by":"publisher","first-page":"2275","DOI":"10.5664\/jcsm.9580","article-title":"Evaluating consumer and clinical sleep technologies: an American Academy of Sleep Medicine update","volume":"17","author":"Schutte-Rodin","year":"2021","journal-title":"Journal of Clinical Sleep Medicine"},{"issue":"1","key":"10.7717\/peerj-cs.1554\/ref-48","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1007\/s13311-020-00955-x","article-title":"Sleep-related breathing disorders: when CPAP is not enough","volume":"18","author":"Selim","year":"2020","journal-title":"Neurotherapeutics"},{"issue":"5","key":"10.7717\/peerj-cs.1554\/ref-49","doi-asserted-by":"publisher","first-page":"758","DOI":"10.1007\/s40846-021-00646-8","article-title":"End-to-end sleep apnea detection using single-lead ECG signal and 1-D residual neural networks","volume":"41","author":"Sharan","year":"2021","journal-title":"Journal of Medical and Biological Engineering"},{"issue":"5","key":"10.7717\/peerj-cs.1554\/ref-50","doi-asserted-by":"publisher","first-page":"116","DOI":"10.1016\/j.compbiomed.2016.08.012","article-title":"An algorithm for sleep apnea detection from single-lead ECG using Hermite basis functions","volume":"77","author":"Sharma","year":"2016","journal-title":"Computers in Biology and Medicine"},{"key":"10.7717\/peerj-cs.1554\/ref-51","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/tim.2021.3062414","article-title":"Multiscale deep neural network for obstructive sleep apnea detection using RR interval from single-lead ECG Signal","volume":"70","author":"Shen","year":"2021","journal-title":"IEEE Transactions on Instrumentation and Measurement"},{"issue":"8","key":"10.7717\/peerj-cs.1554\/ref-52","doi-asserted-by":"publisher","first-page":"132306","DOI":"10.1016\/j.physd.2019.132306","article-title":"Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network","volume":"404","author":"Sherstinsky","year":"2020","journal-title":"Physica D: Nonlinear Phenomena"},{"issue":"14","key":"10.7717\/peerj-cs.1554\/ref-53","doi-asserted-by":"publisher","first-page":"6622","DOI":"10.3390\/app11146622","article-title":"Diagnosis of obstructive sleep apnea from ECG signals using machine learning and deep learning classifiers","volume":"11","author":"Sheta","year":"2021","journal-title":"Applied Sciences"},{"issue":"7","key":"10.7717\/peerj-cs.1554\/ref-54","doi-asserted-by":"publisher","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 Transactions on Biomedical Engineering"},{"issue":"11","key":"10.7717\/peerj-cs.1554\/ref-55","doi-asserted-by":"publisher","first-page":"1599","DOI":"10.5664\/jcsm.8020","article-title":"Predicting nondiagnostic home sleep apnea tests using machine learning","volume":"15","author":"Stretch","year":"2019","journal-title":"Journal of Clinical Sleep Medicine"},{"issue":"11","key":"10.7717\/peerj-cs.1554\/ref-56","doi-asserted-by":"publisher","first-page":"1998","DOI":"10.1109\/tnsre.2017.2721116","article-title":"DeepSleepNet: a model for automatic sleep stage scoring based on Raw single-channel EEG","volume":"25","author":"Supratak","year":"2017","journal-title":"IEEE Transactions on Neural Systems and Rehabilitation Engineering"},{"issue":"4","key":"10.7717\/peerj-cs.1554\/ref-57","doi-asserted-by":"publisher","first-page":"762","DOI":"10.1109\/tbcas.2018.2824659","article-title":"Online obstructive sleep apnea detection on medical wearable sensors","volume":"12","author":"Surrel","year":"2018","journal-title":"IEEE Transactions on Biomedical Circuits and Systems"},{"issue":"10","key":"10.7717\/peerj-cs.1554\/ref-58","doi-asserted-by":"publisher","first-page":"1267","DOI":"10.1164\/rccm.201808-1493oc","article-title":"The combination of atomoxetine and Oxybutynin greatly reduces obstructive sleep apnea severity. A randomized, placebo-controlled, double-blind crossover trial","volume":"199","author":"Taranto-Montemurro","year":"2019","journal-title":"American Journal of Respiratory and Critical Care Medicine"},{"issue":"47","key":"10.7717\/peerj-cs.1554\/ref-59","doi-asserted-by":"publisher","first-page":"475","DOI":"10.3346\/jkms.2020.35.e399","article-title":"Identification of sleep apnea severity based on deep learning from a short-term normal ECG","volume":"35","author":"Urtnasan","year":"2020","journal-title":"Journal of Korean Medical Science"},{"issue":"9","key":"10.7717\/peerj-cs.1554\/ref-60","doi-asserted-by":"publisher","first-page":"2269","DOI":"10.1109\/tbme.2015.2422378","article-title":"A novel algorithm for the automatic detection of sleep apnea from single-lead ECG","volume":"62","author":"Varon","year":"2015","journal-title":"IEEE Transactions on Biomedical Engineering"},{"key":"10.7717\/peerj-cs.1554\/ref-61","doi-asserted-by":"publisher","first-page":"e7731","DOI":"10.7717\/peerj.7731","article-title":"Sleep apnea detection from a single-lead ECG signal with automatic feature-extraction through a modified LeNet-5 convolutional neural network","volume":"7","author":"Wang","year":"2019","journal-title":"PeerJ"},{"issue":"6","key":"10.7717\/peerj-cs.1554\/ref-62","doi-asserted-by":"publisher","first-page":"105211","DOI":"10.1016\/j.compbiomed.2022.105211","article-title":"BI\u2014directional long short-term memory for automatic detection of sleep apnea events based on single channel EEG signal","volume":"142","author":"Wang","year":"2022","journal-title":"Computers in Biology and Medicine"},{"key":"10.7717\/peerj-cs.1554\/ref-63","doi-asserted-by":"publisher","first-page":"1","DOI":"10.21203\/rs.3.rs-558448\/v1","article-title":"A robust sleep apnea-hypopnea syndrome automated detection method based on fuzzy entropy using single lead-EEG signals","author":"Wang","year":"2021","journal-title":"Research Square"},{"issue":"8","key":"10.7717\/peerj-cs.1554\/ref-64","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1016\/j.ymeth.2022.04.017","article-title":"Detection of pediatric obstructive sleep apnea using a multilayer perceptron model based on single-channel Oxygen Saturation or clinical features","volume":"204","author":"Wu","year":"2022","journal-title":"Methods"},{"issue":"15","key":"10.7717\/peerj-cs.1554\/ref-65","doi-asserted-by":"publisher","first-page":"105124","DOI":"10.1016\/j.compbiomed.2021.105124","article-title":"Obstructive sleep apnea detection from single-lead electrocardiogram signals using one-dimensional squeeze-and-excitation residual group network","volume":"140","author":"Yang","year":"2022","journal-title":"Computers in Biology and Medicine"},{"issue":"6","key":"10.7717\/peerj-cs.1554\/ref-66","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1016\/j.inffus.2019.06.024","article-title":"Multi-class arrhythmia detection from 12-lead varied-length ECG using attention-based time-incremental convolutional neural network","volume":"53","author":"Yao","year":"2020","journal-title":"Information Fusion"},{"issue":"3","key":"10.7717\/peerj-cs.1554\/ref-67","doi-asserted-by":"publisher","first-page":"1080","DOI":"10.1161\/cir.0000000000000988","article-title":"Obstructive sleep apnea and cardiovascular disease: a scientific statement from the American Heart Association","volume":"144","author":"Yeghiazarians","year":"2021","journal-title":"Circulation"},{"issue":"7","key":"10.7717\/peerj-cs.1554\/ref-68","doi-asserted-by":"publisher","first-page":"3447","DOI":"10.1109\/jbhi.2022.3156585","article-title":"CMS2-Net: semi-supervised sleep staging for diverse obstructive sleep apnea severity","volume":"26","author":"Zhang","year":"2022","journal-title":"IEEE Journal of Biomedical and Health Informatics"}],"container-title":["PeerJ Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/peerj.com\/articles\/cs-1554.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/peerj.com\/articles\/cs-1554.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/peerj.com\/articles\/cs-1554.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/peerj.com\/articles\/cs-1554.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,29]],"date-time":"2023-09-29T09:06:34Z","timestamp":1695978394000},"score":1,"resource":{"primary":{"URL":"https:\/\/peerj.com\/articles\/cs-1554"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,29]]},"references-count":68,"alternative-id":["10.7717\/peerj-cs.1554"],"URL":"https:\/\/doi.org\/10.7717\/peerj-cs.1554","archive":["CLOCKSS","LOCKSS","Portico"],"relation":{},"ISSN":["2376-5992"],"issn-type":[{"value":"2376-5992","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,29]]},"article-number":"e1554"}}