{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,11]],"date-time":"2025-11-11T06:37:46Z","timestamp":1762843066432,"version":"build-2065373602"},"reference-count":46,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,11,11]],"date-time":"2025-11-11T00:00:00Z","timestamp":1762819200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Artif. Intell."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Sleep disorders pose significant risks to patient safety, yet traditional polysomnography imposes substantial discomfort and laboratory constraints. We developed a non-invasive multimodal monitoring system for real-time sleep pathology detection.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We integrated facial expression analysis via deep convolutional neural networks with audio signal processing for breathing pattern detection. Heterogeneous data streams were unified into dynamic graph representations, with graph neural networks modeling spatiotemporal patterns of sleep pathologies.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The system accurately detected sleep apnea, restless leg syndrome, and cardiovascular irregularities with 10.7-s average delay and 94.6% clinical agreement, achieving diagnostic accuracy comparable to polysomnography.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This framework enables continuous non-invasive monitoring for point-of-care screening and home-based management, potentially expanding sleep medicine access for underserved populations.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/frai.2025.1681759","type":"journal-article","created":{"date-parts":[[2025,11,11]],"date-time":"2025-11-11T06:32:54Z","timestamp":1762842774000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Real-time sleep disorder monitoring design using dynamic temporal graphs with facial and acoustic feature fusion"],"prefix":"10.3389","volume":"8","author":[{"given":"Fei","family":"Pei","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ying","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiangqiang","family":"Fu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hong","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2025,11,11]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"36110","DOI":"10.1109\/ACCESS.2024.3374408","article-title":"Applying machine learning algorithms for the classification of sleep disorders","volume":"12","author":"Alshammari","year":"2024","journal-title":"IEEE Access"},{"key":"B2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/ECCE64574.2025.11013855","article-title":"\u201cAn empirical machine learning approach towards effective sleep disorder prediction,\u201d","volume-title":"2025 International Conference on Electrical, Computer and Communication Engineering (ECCE)","author":"Anny","year":"2025"},{"key":"B3","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":"2023","journal-title":"Neural Comp. 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