{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,24]],"date-time":"2026-10-24T16:14:44Z","timestamp":1792858484498,"version":"4.5.0"},"reference-count":37,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2025,4,10]],"date-time":"2025-04-10T00:00:00Z","timestamp":1744243200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Innovative Human Resource Development for Local Intellectualization program","award":["IITP-2025-RS-2022-00156361"],"award-info":[{"award-number":["IITP-2025-RS-2022-00156361"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Obstructive sleep apnea (OSA) is common among older populations and individuals with cardiovascular diseases. OSA diagnosis is primarily conducted using polysomnography or recommended home sleep apnea test (HSAT) devices. Wireless wearable devices have emerged as promising tools for OSA screening and follow-up. This study introduces a novel automated algorithm for detecting OSA using abdominal movement signals and acceleration data collected by a wireless abdomen-worn sensor (Soomirang). Thirty-seven subjects underwent overnight monitoring using an HSAT device and the Soomirang system simultaneously. Normal and apnea events were classified using an MLP-Mixer deep learning model based on Soomirang data, which was also used to estimate total sleep time (ST). Pearson correlation and Bland\u2013Altman analyses were conducted to evaluate the agreement of ST and the apnea\u2013hypopnea index (AHI) calculated by the HSAT device and Soomirang. ST demonstrated a correlation of 0.9 with an average time difference of 7.5 min, while AHI showed a correlation of 0.95 with an average AHI difference of 3. The accuracy, sensitivity, and specificity of the Soomirang for detecting OSA were 97.14%, 100%, and 95.45% at AHI \u2265 15, respectively. The proposed algorithm, utilizing data from a wireless abdomen-worn device exhibited excellent performance in detecting moderate to severe OSA. The findings underscored the potential of a simple device as an accessible and effective tool for OSA screening and follow-up.<\/jats:p>","DOI":"10.3390\/s25082412","type":"journal-article","created":{"date-parts":[[2025,4,10]],"date-time":"2025-04-10T11:26:41Z","timestamp":1744284401000},"page":"2412","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["An Automated Algorithm for Obstructive Sleep Apnea Detection Using a Wireless Abdomen-Worn Sensor"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-1338-9587","authenticated-orcid":false,"given":"Thi Hang","family":"Dang","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, Ulsan National Institute of Science and Technology, 50, UNIST-gil, Ulsan 44919, Republic of Korea"},{"name":"SB Solutions Inc., Ulsan National Institute of Science and Technology, Ulsan 44919, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6883-2773","authenticated-orcid":false,"given":"Seong-mun","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Ulsan National Institute of Science and Technology, 50, UNIST-gil, Ulsan 44919, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-5060-1746","authenticated-orcid":false,"given":"Min-seong","family":"Choi","sequence":"additional","affiliation":[{"name":"SB Solutions Inc., Ulsan National Institute of Science and Technology, Ulsan 44919, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sung-nam","family":"Hwan","sequence":"additional","affiliation":[{"name":"SB Solutions Inc., Ulsan National Institute of Science and Technology, Ulsan 44919, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hyung-ki","family":"Min","sequence":"additional","affiliation":[{"name":"SB Solutions Inc., Ulsan National Institute of Science and Technology, Ulsan 44919, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Franklin","family":"Bien","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Ulsan National Institute of Science and Technology, 50, UNIST-gil, Ulsan 44919, Republic of Korea"},{"name":"SB Solutions Inc., Ulsan National Institute of Science and Technology, Ulsan 44919, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,4,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1389","DOI":"10.1001\/jama.2020.3514","article-title":"Diagnosis and management of obstructive sleep apnea: A review","volume":"323","author":"Gottlieb","year":"2020","journal-title":"JAMA"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"479","DOI":"10.5664\/jcsm.6506","article-title":"Clinical practice guideline for diagnostic testing for adult obstructive sleep apnea: An American Academy of Sleep Medicine clinical practice guideline","volume":"13","author":"Kapur","year":"2017","journal-title":"J. Clin. Sleep Med."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"e56","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"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2101649","DOI":"10.1183\/13993003.01649-2021","article-title":"Sleep apnoea in the elderly: A great challenge for the future","volume":"59","author":"Osorio","year":"2022","journal-title":"Eur. Respir. J."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1183\/23120541.00053-2023","article-title":"Prevalence, treatment and determinants of obstructive sleep apnoea and its symptoms in a population-based French cohort","volume":"9","author":"Balagny","year":"2023","journal-title":"ERJ Open Res."},{"key":"ref_6","first-page":"257","article-title":"The public health burden of obstructive sleep apnea","volume":"14","author":"Faria","year":"2021","journal-title":"Sleep Sci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"e58187","DOI":"10.2196\/58187","article-title":"Detection of sleep apnea using wearable AI: Systematic review and meta-analysis","volume":"26","author":"Aslam","year":"2024","journal-title":"J. Med. Internet Res."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Moheimani, R., Hosseini, P., Mohammadi, S., and Dalir, H. (2022). Recent advances on capacitive proximity sensors: From design and materials to creative applications. C, 8.","DOI":"10.3390\/c8020026"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Abdulsadig, R.S., Singh, S., Patel, Z., and Rodriguez-Villegas, E. (2022, January 11\u201315). Sleep posture detection using an accelerometer placed on the neck. Proceedings of the 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Glasgow, Scotland, UK.","DOI":"10.1109\/EMBC48229.2022.9871300"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Friederich, A.R., Audu, M.L., and Triolo, R.J. (2022). Trunk Posture from Randomly Oriented Accelerometers. Sensors, 22.","DOI":"10.3390\/s22197690"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Schipper, F., Grassi, A., Ross, M., Cerny, A., Anderer, P., Hermans, L., van Meulen, F., Leentjens, M., Schoustra, E., and Bosschieter, P. (2024). Overnight Sleep Staging Using Chest-Worn Accelerometry. Sensors, 24.","DOI":"10.3390\/s24175717"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3433987","article-title":"Machine learning for sleep apnea detection with unattended sleep monitoring at home","volume":"2","author":"Kristiansen","year":"2021","journal-title":"ACM Trans. Comput. Healthc."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Barroso-Garc\u00eda, V., Fern\u00e1ndez-Poyatos, M., Sahelices, B., \u00c1lvarez, D., Gozal, D., Hornero, R., and Guti\u00e9rrez-Tobal, G.C. (2023). Prediction of the sleep apnea severity using 2D-convolutional neural networks and respiratory effort signals. Diagnostics, 13.","DOI":"10.3390\/diagnostics13203187"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Yeh, E., Wong, E., Tsai, C.-W., Gu, W., Chen, P.-L., Leung, L., Wu, I.-C., Strohl, K.P., Folz, R.J., and Yar, W. (2021). Detection of obstructive sleep apnea using Belun Sleep Platform wearable with neural network-based algorithm and its combined use with STOP-Bang questionnaire. PLoS ONE, 16.","DOI":"10.1371\/journal.pone.0258040"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"eadg9671","DOI":"10.1126\/sciadv.adg9671","article-title":"At-home wireless sleep monitoring patches for the clinical assessment of sleep quality and sleep apnea","volume":"9","author":"Kwon","year":"2023","journal-title":"Sci. Adv."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"100106","DOI":"10.1016\/j.smhl.2020.100106","article-title":"Development and feasibility test of a capacitive belt sensor for noninvasive respiration monitoring in different postures","volume":"16","author":"Kim","year":"2020","journal-title":"Smart Health"},{"key":"ref_17","first-page":"012033","article-title":"Wearable respiratory strain monitoring system based on textile-based capacitive strain sensor","volume":"Volume 1570","author":"Jun","year":"2020","journal-title":"Journal of Physics: Conference Series"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"22641","DOI":"10.1109\/ACCESS.2020.2969227","article-title":"Sleep apnea severity estimation from tracheal movements using a deep learning model","volume":"8","author":"Hafezi","year":"2020","journal-title":"IEEE Access"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"45664","DOI":"10.1109\/ACCESS.2020.2976194","article-title":"A smart bed for non-obtrusive sleep analysis in real world context","volume":"8","author":"Laurino","year":"2020","journal-title":"IEEE Access"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1033","DOI":"10.1007\/s11325-021-02465-2","article-title":"Sleep apnea and respiratory anomaly detection from a wearable band and oxygen saturation","volume":"26","author":"Ganglberger","year":"2022","journal-title":"Sleep Breath."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Papini, G.B., Fonseca, P., van Gilst, M.M., Bergmans, J.W., Vullings, R., and Overeem, S. (2020). Wearable monitoring of sleep-disordered breathing: Estimation of the apnea\u2013hypopnea index using wrist-worn reflective photoplethysmography. Sci. Rep., 10.","DOI":"10.1038\/s41598-020-69935-7"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1533","DOI":"10.2147\/NSS.S323286","article-title":"A single-center validation of the accuracy of a photoplethysmography-based smartwatch for screening obstructive sleep apnea","volume":"13","author":"Chen","year":"2021","journal-title":"Nat. Sci. Sleep"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"ElMoaqet, H., Eid, M., Glos, M., Ryalat, M., and Penzel, T. (2020). Deep recurrent neural networks for automatic detection of sleep apnea from single channel respiration signals. Sensors, 20.","DOI":"10.3390\/s20185037"},{"key":"ref_24","first-page":"24261","article-title":"MLP-Mixer: An all-mlp architecture for vision","volume":"34","author":"Tolstikhin","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Huang, Z., and He, K. (July, January 30). GRU-TSMixers: Sleep Apnea and Hypopnea Detection Based on Multi Scale MLP-Mixers. Proceedings of the 2024 International Joint Conference on Neural Networks (IJCNN), Yokohama, Japan.","DOI":"10.1109\/IJCNN60899.2024.10650636"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Zhong, S., Song, S., Zhuo, W., Li, G., Liu, Y., and Chan, S.H.G. (2023). A multi-scale decomposition mlp-mixer for time series analysis. arXiv.","DOI":"10.14778\/3654621.3654637"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Abdullah, A.A., Hassan, M.M., and Mustafa, Y.T. (2023). Uncertainty quantification for MLP-Mixer using Bayesian deep learning. Appl. Sci., 13.","DOI":"10.3390\/app13074547"},{"key":"ref_28","unstructured":"Gallagher, N.B. (2020). Savitzky-golay Smoothing and Differentiation Filter, Eigenvector Research Incorporated."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Ross, R., Mongan, W.M., O\u2019Neill, P., Rasheed, I., Fontecchio, A., Dion, G., and Dandekar, K.R. (2021, January 12\u201316). An adaptively parameterized algorithm estimating respiratory rate from a passive wearable RFID smart garment. Proceedings of the 2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC), Madrid, Spain.","DOI":"10.1109\/COMPSAC51774.2021.00110"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"4229924","DOI":"10.1155\/2023\/4229924","article-title":"Mathematical analysis and performance evaluation of the gelu activation function in deep learning","volume":"2023","author":"Lee","year":"2023","journal-title":"J. Math."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Siami-Namini, S., Tavakoli, N., and Namin, A.S. (2019, January 9\u201312). The performance of LSTM and BiLSTM in forecasting time series. Proceedings of the 2019 IEEE International Conference on Big Data (Big Data), Los Angeles, CA, USA.","DOI":"10.1109\/BigData47090.2019.9005997"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Hassanzad, M., and Hajian-Tilaki, K. (2024). Methods of determining optimal cut-point of diagnostic biomarkers with application of clinical data in ROC analysis: An update review. BMC Med. Res. Methodol., 24.","DOI":"10.1186\/s12874-024-02198-2"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Halimu, C., Kasem, A., and Newaz, S.S. (2019, January 25\u201328). Empirical comparison of area under ROC curve (AUC) and Mathew correlation coefficient (MCC) for evaluating machine learning algorithms on imbalanced datasets for binary classification. Proceedings of the 3rd International Conference on Machine Learning and Soft Computing, Da Lat, Vietnam.","DOI":"10.1145\/3310986.3311023"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Rainio, O., Teuho, J., and Kl\u00e9n, R. (2024). Evaluation metrics and statistical tests for machine learning. Sci. Rep., 14.","DOI":"10.1038\/s41598-024-56706-x"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1611","DOI":"10.5664\/jcsm.8592","article-title":"Belun Ring Platform: A novel home sleep apnea testing system for assessment of obstructive sleep apnea","volume":"16","author":"Gu","year":"2020","journal-title":"J. Clin. Sleep Med."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"zsac028","DOI":"10.1093\/sleep\/zsac028","article-title":"A multicentric validation study of a novel home sleep apnea test based on peripheral arterial tonometry","volume":"45","author":"Massie","year":"2022","journal-title":"Sleep"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1149","DOI":"10.5664\/jcsm.8462","article-title":"Screening of obstructive sleep apnea in patients who snore using a patch-type device with electrocardiogram and three-axis accelerometer","volume":"16","author":"Hsu","year":"2020","journal-title":"J. Clin. Sleep Med."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/8\/2412\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:12:30Z","timestamp":1760029950000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/8\/2412"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,10]]},"references-count":37,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2025,4]]}},"alternative-id":["s25082412"],"URL":"https:\/\/doi.org\/10.3390\/s25082412","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,10]]}}}