Supriya S, Siuly S, Wang H, Zhang Y. Eeg sleep stages analysis and classification based on weighed complex network features. IEEE Trans Emerg Top Comput Intell. 2021;5:236–46.

Google Scholar 

White DP. Sleep apnea. Proc Am Thorac Soc 2006;3:124–8.

Google Scholar 

Benjafield AV, et al. Estimation of the global prevalence and burden of obstructive sleep apnoea: a literature-based analysis. Lancet Respir Med. 2019;7:687–98.

Google Scholar 

Berry RB, et al. The AASM manual for the scoring of sleep and associated events. Rules, terminology and technical specifications. Darien: American Academy of Sleep Medicine; 2012. p. 176.

Javaheri S, Dempsey J. Central sleep apnea. Compreh Physiol. 2013;3:141–63.

Google Scholar 

Malhotra A, Owens RL. What is central sleep apnea? Respir Care. 2010;55:1168–78.

Google Scholar 

Iber C, Davies SF, Chapman RC, Mahowald MM. A possible mechanism for mixed apnea in obstructive sleep apnea. Chest. 1986;89:800–5.

Google Scholar 

American Academy of Sleep Medicine Task Force, et al. Sleep-related breathing disorders in adults: recommendations for syndrome definition and measurement techniques in clinical research. the report of an American Academy of Sleep Medicine Task Force. Sleep. 1999;22:667.

Kushida CA, et al. Practice parameters for the indications for polysomnography and related procedures: an update for 2005. Sleep. 2005;28:499–523.

Google Scholar 

Abrahamyan L, et al. Diagnostic accuracy of level IV portable sleep monitors versus polysomnography for obstructive sleep apnea: a systematic review and meta-analysis. Sleep Breath. 2018;22:593–611.

Google Scholar 

Collop N, et al. Portable monitoring task force of the American Academy of Sleep Medicine clinical guidelines for the use of unattended portable monitors in the diagnosis of obstructive sleep apnea in adult patients. J Clin Sleep Med 2007;3:737–47.

Mostafa SS, Mendonça F, Ravelo-García GA, Morgado-Dias F. A systematic review of detecting sleep apnea using deep learning. Sensors 2019;19:4934.

Collop NA. Scoring variability between polysomnography technologists in different sleep laboratories. Sleep Med. 2002;3:43–7.

Google Scholar 

Hayano J, et al. Quantitative detection of sleep apnea with wearable watch device. PLoS ONE. 2020;15: e0237279.

Google Scholar 

O’Mahony AM, Garvey JF, McNicholas WT. Technologic advances in the assessment and management of obstructive sleep apnoea beyond the apnoea-hypopnoea index: a narrative review. J Thorac Dis. 2020;12:5020.

Google Scholar 

Choi SH, et al. Real-time apnea-hypopnea event detection during sleep by convolutional neural networks. Comput Biol Med. 2018;100:123–31.

Google Scholar 

LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521:436–44.

Google Scholar 

Urtnasan E, Park J-U, Lee K-J. Multiclass classification of obstructive sleep apnea/hypopnea based on a convolutional neural network from a single-lead electrocardiogram. Physiol Meas. 2018;39: 065003.

Google Scholar 

Jiang D, Ma Y, Wang Y. A multi-scale parallel convolutional neural network for automatic sleep apnea detection using single-channel EEG signals. In: 2018 11th International congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI). IEEE; 2018. p. 1–5.

Memis G, Sert M. Multimodal classification of obstructive sleep apnea using feature level fusion. In: 2017 IEEE 11th International conference on semantic computing (ICSC). IEEE; 2017; p. 85–88.

Dean DA, et al. Scaling up scientific discovery in sleep medicine: the national sleep research resource. Sleep. 2016;39:1151–64.

Google Scholar 

Chen X, et al. Racial/ethnic differences in sleep disturbances: the multi-ethnic study of atherosclerosis (MESA). Sleep. 2015;38:877–88.

Google Scholar 

Penzel T, Moody GB, Mark RG, Goldberger AL, Peter JH. The apnea-ECG database. IEEE; 2000. p. 255–8.

McNicholas W, et al. St. Vincents University Hospital/University College Dublin Sleep Apnea Database PhysioNet; 2004. http://physionet.org.

Ichimaru Y, Moody G. Development of the polysomnographic database on CD-ROM. Psychiatry Clin Neurosci.. 1999;53:175–7.

Google Scholar 

Redline S, et al. The childhood adenotonsillectomy trial (CHAT): rationale, design, and challenges of a randomized controlled trial evaluating a standard surgical procedure in a pediatric population. Sleep. 2011;34:1509–17.

Google Scholar 

Korompili, G, et al. PSG-Audio; 2022.

Korompili G, et al. PSG-Audio, a scored polysomnography dataset with simultaneous audio recordings for sleep apnea studies. Sci Data. 2021;8:197.

Google Scholar 

Quan SF, et al. The sleep heart health study: design, rationale, and methods. Sleep. 1997;20:1077–85.

Google Scholar 

Patro S., Sahu KK. Normalization: a preprocessing stage. arXiv preprint; 2015. arXiv:1503.06462.

Aguiar-Conraria L, Soares MJ. The continuous wavelet transform: moving beyond uni- and bivariate analysis. J Econ Surv. 2014;28:344–75.

Google Scholar 

Griffin D, Lim J. Signal estimation from modified short-time Fourier Transform. IEEE Trans Acoust Speech Signal Process. 1984;32:236–43.

Google Scholar 

Gabor, D. Theory of communication. Part 1: the analysis of information. J Inst Electr Eng Part III Radio Commun Eng. 1946;93:429–41.

Google Scholar 

Cho S-H, Jang G, Kwon S-H. Time–frequency analysis of power-quality disturbances via the Gabor–Wigner transform. IEEE Trans Power Deliv. 2009;25:494–9.

Google Scholar 

Yang W, Fan J, Wang X, Liao Q. Sleep apnea and hypopnea events detection based on airflow signals using LSTM network. In: 2019 41st annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

Jansri U, Tretriluxana S. Effect of resampling techniques on deep learning model training in sleep apnea classification. In: 2022 International electrical engineering congress (iEECON). IEEE; 2022. p. 1–4.

ElMoaqet H, Eid M, Glos M, Ryalat M, Penzel T. Deep recurrent neural networks for automatic detection of sleep apnea from single channel respiration signals. Sensors. 2020;20:5037.

Google Scholar 

McCloskey S, Haidar R, Koprinska I, Jeffries B. Detecting hypopnea and obstructive apnea events using convolutional neural networks on wavelet spectrograms of nasal airflow. In: Advances in knowledge discovery and data mining. PAKDD 2018. Lecture notes in computer science; 2018. p. 361–372.

Yue H, et al. Deep learning for diagnosis and classification of obstructive sleep apnea: a nasal airflow-based multi-resolution residual network. Nat Sci Sleep. 2021;13:361–73.

Wu Y, et al. A novel approach to diagnose sleep apnea using enhanced frequency extraction network. Comput Methods Programs Biomed. 2021;206:106119.

Google Scholar 

Jiménez-García J, et al. A 2d convolutional neural network to detect sleep apnea in children using airflow and oximetry. Comput Biol Med. 2022;147: 105784.

Google Scholar 

Haidar R, Koprinska I, Jeffries B. Sleep apnea event prediction using convolutional neural networks and Markov chains. In: 2020 International joint conference on neural networks (IJCNN). IEEE; 2020. p. 1–8.

Peng D, et al. A bimodal feature fusion convolutional neural network for detecting obstructive sleep apnea/hypopnea from nasal airflow and oximetry signals. Artif Intell Med. 2024;150:102808.

Graves A, Graves, A. Long short-term memory. In: Supervised sequence labelling with recurrent neural networks. Berlin: Springer; 2012;37–45.

Leino A, et al. Neural network analysis of nocturnal SpO2 signal enables easy screening of sleep apnea in patients with acute cerebrovascular disease. Sleep Med. 2021;79:71–8.

Google Scholar 

Vaquerizo-Villar F, et al. A convolutional neural network architecture to enhance oximetry ability to diagnose pediatric obstructive sleep apnea. IEEE J Biomed Health Inf. 2021;25:2906–16.

Google Scholar 

John A, Nundy KK, Cardiff B, Joh, D. SomnNET: an SpO2 based deep learning network for sleep apnea detection in smartwatches. In: Annual international conference of the IEEE Engineering in Medicine and Biology Society. IEEE; 2021. p. 1961–4.

Wu Y, Jia Y, Ning X, Xu Z, Rosen D. Detection of pediatric obstructive sleep apnea using a multilayer perceptron model based on single-channel oxygen saturation or clinical features. Methods. 2022;204:361–7.

Google Scholar 

Paul, T, et al. ECG and SpO2 signal-based real-time sleep apnea detection using feed-forward artificial neural network. AMIA Jt Summits Transl Sci Proc 2022:379–385.

Hassan, O, et al. A multi-sensor based automatic sleep apnea detection system for adults using neural network inference on FPGA. In: 2022 IEEE International symposium on medical measurements and applications (MeMeA). IEEE; 2022. p. 1–6.

Ayalew MP, Nemomssa HD, Simegn GL. Sleep apnea syndrome detection and classification of severity level from ECG and SpO2 signals. Health Technol. 2021;5:13.

Google Scholar 

Sharma P, Jalali A, Majmudar M, Rajput KS, Selvaraj N. Deep-learning based sleep apnea detection using SpO2 and pulse rate. In: Annu Int Conf IEEE Eng Med Biol Soc. IEEE; 2022. p. 2611–4.

Malhotra A, White DP. Obstructive sleep apnoea. Lancet. 2002;360:237–45.

Google Scholar 

Terrill PI. A review of approaches for analysing obstructive sleep apnoea-related patterns in pulse oximetry data. Respirology. 2020;25:475–85.

Google Scholar 

Mashrur FR, Islam MS, Saha DK, Islam SR, Moni MA. SCNN: scalogram-based convolutional neural network to detect obstructive sleep apnea using single-lead electrocardiogram signals. Comput Biol Med. 2021;134: 104532.

Google Scholar 

Niroshana SI, Zhu X, Nakamura K, Chen W. A fused-image-based approach to detect obstructive sleep apnea using a single-lead ECG and a 2D convolutional neural network. PLoS ONE. 2021;16: e0250618.

Google Scholar 

Gupta K, Bajaj V, Ansari IA. Osacn-net: automated classification of sleep apnea using deep learning model and smoothed gabor spectrograms of ecg signal. IEEE Trans Instrum Meas. 2021;71:1–9.

Google Scholar 

Ayatollahi A, Afrakhteh S, Soltani F, Saleh E. Sleep apnea detection from ECG signal using deep CNN-based structures. Evol Syst. 2023;14:191–206.

Google Scholar 

Sharan RV, Berkovsky S, Xiong H, Coiera E. ECG-derived heart rate variability interpolation and 1-D convolutional neural networks for detecting sleep apnea. In: Annu Int Conf IEEE Eng Med Biol Soc. IEEE;2020. p. 637–40.

Chen X, Chen Y, Ma W, Fan X, Li Y. Toward sleep apnea detection with lightweight multi-scaled fusion network. Knowl. Based Syst. 2022;247: 108783.

Google Scholar 

Shen Q, Qin H, Wei K, Liu G. Multiscale deep neural network for obstructive sleep apnea detection using RR interval from single-lead ECG signal. IEEE Trans Instrum Meas. 2021;70:1–13.

Google Scholar 

Iwasaki A, et al. Screening of sleep apnea based on heart rate variability and long short-term memory. Sleep Breath. 2021;25:1821–9.

Google Scholar 

Faust O, Barika R, Shenfield A, Ciaccio EJ, Acharya UR. Accurate detection of sleep apnea with long short-term memory network based on RR interval signals. Knowl Based Syst. 2021;212: 106591.

Google Scholar 

Zarei A, Beheshti H, Asl BM. Detection of sleep apnea using deep neural networks and single-lead ECG signals. Biomed Signal Process Control. 2022;71: 103125.

Google Scholar 

Almutairi H, Hassan GM, Datta A. Classification of obstructive sleep apnoea from single-lead ECG signals using convolutional neural and long short term memory networks. Biomed Signal Process Control. 2021;69: 102906.

Google Scholar 

Bahrami M, Forouzanfar M. Deep learning forecasts the occurrence of sleep apnea from single-lead ECG. Cardiovasc Eng Technol. 2022;13(6):809–815.

Yang Q, Zou L, Wei K, Liu G. Obstructive sleep apnea detection from single-lead electrocardiogram signals using one-dimensional squeeze-and-excitation residual group network. Comput Biol Med. 2022;140:105124.

Google Scholar 

Sharan RV, Berkovsky S, Xiong H, Coiera E. End-to-end sleep apnea detection using single-lead ECG signal and 1-D residual neural networks. J Med Biol Eng. 2021;41:758–66.

Google Scholar 

Liu H, Cui S, Zhao X, Cong F. Detection of obstructive sleep apnea from single-channel ECG signals using a CNN-transformer architecture. Biomed Signal Process Control. 2023;82: 104581.

Google Scholar 

Hu S, Cai W, Gao T, Wang M. A hybrid transformer model for obstructive sleep apnea detection based on self-attention mechanism using single-lead ECG. IEEE Trans Instrum Meas. 2022;71:1–11.

Google Scholar 

Feng K, Qin H, Wu S, Pan W, Liu G. A sleep apnea detection method based on unsupervised feature learning and single-lead electrocardiogram. IEEE Trans Instrum Meas. 2020;70:1–12.

Google Scholar 

Li Z, et al. 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. Sleep Breath. 2021;25(4):2065–72.

Meng L, et al. Enhancing dynamic ECG heartbeat classification with lightweight transformer model. Artif Intell Med. 2022;124: 102236.

Google Scholar 

Guilleminault C, Winkle R, Connolly S, Melvin K, Tilkian A. Cyclical variation of the heart rate in sleep apnoea syndrome: mechanisms, and usefulness of 24 h electrocardiography as a screening technique. Lancet. 1984;323:126–31.

Google Scholar 

Heneghan C, et al. Electrocardiogram recording as a screening tool for sleep disordered breathing. J Clin Sleep Med. 2008;4:223–8.

Google Scholar 

Malik M, et al. Heart rate variability: standards of measurement, physiological interpretation, and clinical use. Eur Heart J. 1996;17:354–81.

Google Scholar 

Nakayama C, et al. Obstructive sleep apnea screening by heart rate variability-based apnea/normal respiration discriminant model. Physiol Meas. 2019;40: 125001.

Google Scholar 

Goldberger AL, et al. Physiobank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals. Circulation. 2000;101:e215–e220.

He K, Zhang X, Ren, S, Sun J. Deep residual learning for image recognition. In: 2016 IEEE Conference on computer vision and pattern recognition (CVPR). IEEE; 2016. p. 770–8.

Vaswani A, et al. Attention is all you need. In: Advances in neural information processing systems 30 (NIPS 2017). p. 30.

Bank D, Koenigstein N, Giryes R. Autoencoders. arXiv preprint; 2020. arXiv:2003.05991.

Mahmud T, et al. Sleep apnea event detection from sub-frame based feature variation in EEG signal using deep convolutional neural network. In: Annu Int Conf IEEE Eng Med Biol Soc. IEEE;2020. p. 5580–3.

Mahmud T, Khan IA, Mahmud TI, Fattah SA, Zhu W-P. Sleep apnea detection from variational mode decomposed eeg signal using a hybrid CNN-BILSTM. IEEE Access. 2021;9:102355–67.