{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,25]],"date-time":"2026-01-25T03:10:06Z","timestamp":1769310606527,"version":"3.49.0"},"reference-count":48,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2023,5,12]],"date-time":"2023-05-12T00:00:00Z","timestamp":1683849600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>An increasing number of patients and a lack of awareness about obstructive sleep apnea is a point of concern for the healthcare industry. Polysomnography is recommended by health experts to detect obstructive sleep apnea. The patient is paired up with devices that track patterns and activities during their sleep. Polysomnography, being a complex and expensive process, cannot be adopted by the majority of patients. Therefore, an alternative is required. The researchers devised various machine learning algorithms using single lead signals such as electrocardiogram, oxygen saturation, etc., for the detection of obstructive sleep apnea. These methods have low accuracy, less reliability, and high computation time. Thus, the authors introduced two different paradigms for the detection of obstructive sleep apnea. The first is MobileNet V1, and the other is the convergence of MobileNet V1 with two separate recurrent neural networks, Long-Short Term Memory and Gated Recurrent Unit. They evaluate the efficacy of their proposed method using authentic medical cases from the PhysioNet Apnea-Electrocardiogram database. The model MobileNet V1 achieves an accuracy of 89.5%, a convergence of MobileNet V1 with LSTM achieves an accuracy of 90%, and a convergence of MobileNet V1 with GRU achieves an accuracy of 90.29%. The obtained results prove the supremacy of the proposed approach in comparison to the state-of-the-art methods. To showcase the implementation of devised methods in a real-life scenario, the authors design a wearable device that monitors ECG signals and classifies them into apnea and normal. The device employs a security mechanism to transmit the ECG signals securely over the cloud with the consent of patients.<\/jats:p>","DOI":"10.3390\/s23104692","type":"journal-article","created":{"date-parts":[[2023,5,12]],"date-time":"2023-05-12T09:56:18Z","timestamp":1683885378000},"page":"4692","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Efficient Deep Learning Based Hybrid Model to Detect Obstructive Sleep Apnea"],"prefix":"10.3390","volume":"23","author":[{"given":"Prashant","family":"Hemrajani","sequence":"first","affiliation":[{"name":"Computer and Communication Engineering, Manipal University Jaipur, Jaipur 303007, Rajasthan, India"}]},{"given":"Vijaypal Singh","family":"Dhaka","sequence":"additional","affiliation":[{"name":"Computer and Communication Engineering, Manipal University Jaipur, Jaipur 303007, Rajasthan, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5786-2504","authenticated-orcid":false,"given":"Geeta","family":"Rani","sequence":"additional","affiliation":[{"name":"Computer and Communication Engineering, Manipal University Jaipur, Jaipur 303007, Rajasthan, India"}]},{"given":"Praveen","family":"Shukla","sequence":"additional","affiliation":[{"name":"Computer and Communication Engineering, Manipal University Jaipur, Jaipur 303007, Rajasthan, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9743-1701","authenticated-orcid":false,"given":"Durga Prasad","family":"Bavirisetti","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Norwegian University of Science and Technology, 7034 Trondheim, Norway"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,12]]},"reference":[{"key":"ref_1","first-page":"1","article-title":"Sleep apnea detection from single-lead ECG: A comprehensive analysis of machine learning and deep learning algorithms","volume":"71","author":"Bahrami","year":"2022","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1016\/j.amjmed.2018.09.021","article-title":"Sleep Disorders","volume":"132","author":"Pavlova","year":"2019","journal-title":"Am. J. Med."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"809","DOI":"10.1007\/s13239-022-00615-5","article-title":"Deep learning forecasts the occurrence of sleep apnea from single-lead ECG","volume":"13","author":"Bahrami","year":"2022","journal-title":"Cardiovasc. Eng. Technol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"520","DOI":"10.1056\/NEJM199008233230805","article-title":"Sleep disorders and aging","volume":"323","author":"Prinz","year":"1990","journal-title":"N. Engl. J. Med."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Mcclure, K., Erdreich, B., Bates, J.H.T., Mcginnis, R.S., Masquelin, A., and Wshah, S. (2020). Classification and detection of breathing patterns with wearable sensors and deep learning. Sensors, 20.","DOI":"10.3390\/s20226481"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Kim, T., Kim, J.W., and Lee, K. (2018). Detection of sleep disordered breathing severity using acoustic biomarker and machine learning techniques. BioMed Eng. Online, 17.","DOI":"10.1186\/s12938-018-0448-x"},{"key":"ref_7","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":"2015","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2506913","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 Trans. Instrum. Meas."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.cmpb.2019.05.002","article-title":"A RR interval based automated apnea detection approach using residual network","volume":"176","author":"Wang","year":"2019","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_10","unstructured":"(2023, January 12). ResMed Blog Page. Available online: https:\/\/www.resmed.co.in\/blogs\/prevalence-sleep-apnea-india."},{"key":"ref_11","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_12","doi-asserted-by":"crossref","first-page":"102796","DOI":"10.1016\/j.dsp.2020.102796","article-title":"Detection of sleep apnea from heart beat interval and ECG derived respiration signals using sliding mode singular spectrum analysis","volume":"104","author":"Singh","year":"2020","journal-title":"Digit. Signal Process."},{"key":"ref_13","first-page":"215","article-title":"Physionet: Components of a new research resource for complex physiological signals","volume":"101","author":"Goldbergeret","year":"2000","journal-title":"Circulation"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"463","DOI":"10.5664\/jcsm.7676","article-title":"Objective Relationship between Sleep Apnea and Frequency of Snoring Assessed by Machine Learning","volume":"15","author":"Alshaer","year":"2019","journal-title":"J. Clin. Sleep Med."},{"key":"ref_15","doi-asserted-by":"crossref","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 Trans. Biomed. Eng."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Zarei, A., Beheshti, H., and Asl, B.M. (2022). Detection of sleep apnea using deep neural networks and single-lead ECG signals. Biomed. Signal Process. Control, 71.","DOI":"10.1016\/j.bspc.2021.103125"},{"key":"ref_17","first-page":"4000912","article-title":"A sleep apnea detection method based on unsupervised feature learning and single-lead electrocardiogram","volume":"70","author":"Feng","year":"2020","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_18","doi-asserted-by":"crossref","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 Trans. Biomed. Circuits Syst."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1950026","DOI":"10.1142\/S021951941950026X","article-title":"A novel approach osa detection using single-lead ECG scalogram based on deep neural network","volume":"19","author":"Singh","year":"2019","journal-title":"J. Mech. Med. Biol."},{"key":"ref_20","doi-asserted-by":"crossref","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":"J. Clin. Sleep Med."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1036","DOI":"10.1109\/JBHI.2017.2740120","article-title":"Cardiorespiratory model-based data-driven approach for sleep apnea detection","volume":"22","author":"Gutta","year":"2017","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"9768072","DOI":"10.1155\/2019\/9768072","article-title":"Detection of sleep apnea from single-lead ECG signal using a time window artificial neural network","volume":"2019","author":"Wang","year":"2019","journal-title":"BioMed Res. Int."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/j.irbm.2020.05.006","article-title":"Detection of abnormal respiratory events with single channel ECG and hybrid machine learning model in patients with obstructive sleep apnea","volume":"41","author":"Bozkurt","year":"2020","journal-title":"IRBM"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Liang, X., Qiao, X., and Li, Y. (2019, January 24\u201326). Obstructive sleep apnea detection using combination of cnn and lstm techniques. Proceedings of the 2019 IEEE 8th Joint International Information Technology and Artificial Intelligence Conference (ITAIC), Chongqing, China.","DOI":"10.1109\/ITAIC.2019.8785833"},{"key":"ref_25","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":"2013","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_26","unstructured":"Kingma, P., and Ba, J.L. (2015, January 7\u20139). Adam: A method for stochastic optimization. Proceedings of the 3rd International Conference on Learning Representations, ICLR 2015-Conference Track Proceedings, San Diego, CA, USA."},{"key":"ref_27","unstructured":"Ruder, S. (2016). An overview of gradient descent optimization algorithms. arXiv."},{"key":"ref_28","first-page":"295","article-title":"Apnea-hypopnea index prediction using electrocardiogram acquired during the sleep-onset period","volume":"64","author":"Hwang","year":"2016","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_29","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":"2010","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"ref_30","unstructured":"Agarap, A.M.F. (2018). Deep Learning using Rectified Linear Units (ReLU). arXiv."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Mostafa, S.S., Mendon\u00e7a, F., Ravelo-Garc\u00eda, A.G., and Morgado-Dias, F. (2019). A systematic review of detecting sleep apnea using deep learning. Sensors, 19.","DOI":"10.3390\/s19224934"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"4733","DOI":"10.1007\/s00521-018-3833-2","article-title":"Automatic detection of sleep-disordered breathing events using recurrent neural networks from an electrocardiogram signal","volume":"32","author":"Urtnasan","year":"2018","journal-title":"Neural Comput. Appl."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1007\/s10462-021-10033-z","article-title":"A review on weight initialization strategies for neural networks","volume":"55","author":"Narkhede","year":"2022","journal-title":"Artif. Intell. Rev."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1049\/el.2018.7980","article-title":"Trimmed categorical cross-entropy for deep learning with label noise","volume":"55","author":"Rusiecki","year":"2019","journal-title":"Electron. Lett."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Barba-Guaman, L., Eugenio Naranjo, J., and Ortiz, A. (2020). Deep Learning Framework for Vehicle and Pedestrian Detection in Rural Roads on an Embedded GPU. Electronics, 9.","DOI":"10.3390\/electronics9040589"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Ademola, O.A., Leier, M., and Petlenkov, E. (2021). Evaluation of Deep Neural Network Compression Methods for Edge Devices Using Weighted Score-Based Ranking Scheme. Sensors, 21.","DOI":"10.3390\/s21227529"},{"key":"ref_37","unstructured":"Nganga, K. (2023, February 14). Building A Multiclass Image Classifier Using MobilenetV2 and TensorFlow. Available online: https:\/\/www.section.io\/engineering-education\/building-a-multiclass-image-classifier-using-mobilenet-v2-and-tensorflow."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Srinivasu, P.N., Sivasai, J.G., Ijaz, M.F., Bhoi, A.K., Kim, W., and Kang, J.J. (2021). Classification of skin disease using deep learning neural networks with mobilenet v2 and LSTM. Sensors, 21.","DOI":"10.3390\/s21082852"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1169","DOI":"10.1109\/TBME.2012.2186448","article-title":"Application of kernel principal component analysis for single-lead-ECG-derived respiration","volume":"59","author":"Widjaja","year":"2012","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_40","unstructured":"Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning, MIT Press."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"801","DOI":"10.1016\/S0893-6080(05)80125-X","article-title":"Approximation of dynamical systems by continuous time recurrent neural networks","volume":"6","author":"Funahashi","year":"1993","journal-title":"Neural Netw."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","article-title":"Deep learning in neural networks: An overview","volume":"61","author":"Schmidhuber","year":"2015","journal-title":"Neural Netw."},{"key":"ref_43","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_44","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_45","unstructured":"Zaremba, W., and Sutskever, I. (2014). Learning to execute. arXiv."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Yang, W., Fan, J., Wang, X., and Liao, Q. (2019, January 23\u201327). Sleep apnea and hypopnea events detection based on airflow signals using LSTM network. Proceedings of the 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin, Germany.","DOI":"10.1109\/EMBC.2019.8857558"},{"key":"ref_47","doi-asserted-by":"crossref","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":"Comput. Methods Programs Biomed."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.patrec.2008.08.010","article-title":"An experimental comparison of performance measures for classification","volume":"30","author":"Ferri","year":"2009","journal-title":"Pattern Recognit. Lett."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/10\/4692\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:33:39Z","timestamp":1760124819000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/10\/4692"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,12]]},"references-count":48,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2023,5]]}},"alternative-id":["s23104692"],"URL":"https:\/\/doi.org\/10.3390\/s23104692","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,12]]}}}