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Meas."],"published-print":{"date-parts":[[2025,7,31]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>\n                  <jats:italic>Objective<\/jats:italic>. Sleep apnea is a common sleep disorder associated with severe health risks, necessitating accurate and efficient detection methods. <jats:italic>Approach<\/jats:italic>. This study proposes ModelS4Apnea, a deep learning framework for sleep apnea detection from electrocardiogram (ECG) spectrograms, integrating structured state space models (S4) for temporal modeling. The framework consists of a convolutional neural network module for local feature extraction, an S4 module for capturing long-range dependencies, and a classification module for final predictions. <jats:italic>Main results<\/jats:italic>. The model was trained and evaluated on the Apnea-ECG dataset, achieving an accuracy of 0.933, an <jats:italic>F<\/jats:italic>1-score of 0.912, a sensitivity of 0.916, and a specificity of 0.944, outperforming most prior studies while maintaining computational efficiency. <jats:italic>Significance<\/jats:italic>. Compared to existing methods, ModelS4Apnea provides high classification performance with significantly fewer trainable parameters than long short-term memory-based models, reducing training time and memory consumption. The model\u2019s ability to aggregate segment-level predictions enabled perfect per-recording classification, demonstrating its robustness in diagnosing sleep apnea across entire recordings. Moreover, its low memory footprint and fast inference speed make it well-suited for wearable devices, home-based monitoring, and clinical applications, offering a scalable and efficient solution for automated sleep apnea detection. Future work may explore multi-modal data integration, real-world deployment, and further optimizations to enhance its clinical applicability and reliability.<\/jats:p>","DOI":"10.1088\/1361-6579\/adebdd","type":"journal-article","created":{"date-parts":[[2025,7,3]],"date-time":"2025-07-03T18:55:19Z","timestamp":1751568919000},"page":"075002","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["ModelS4Apnea: leveraging structured state space models for efficient sleep apnea detection from ECG signals"],"prefix":"10.1088","volume":"46","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8156-016X","authenticated-orcid":true,"given":"Hasan","family":"Zan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2025,7,11]]},"reference":[{"key":"pmeaadebddbib1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TIM.2022.3151947","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. 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All rights, including for text and data mining, AI training, and similar technologies, are reserved.","name":"copyright_information","label":"Copyright Information"},{"value":"2025-05-01","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2025-07-03","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2025-07-11","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}