{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T06:01:27Z","timestamp":1781157687490,"version":"3.54.1"},"reference-count":59,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T00:00:00Z","timestamp":1781136000000},"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. Bioeng. Biotechnol."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Sleep apnea is a prevalent, yet underdiagnosed, disorder associated with cardiovascular disease, hypertension, and cognitive decline. Although polysomnography (PSG) is the diagnostic gold standard, its high cost &amp;gt; $6,000 per unit, invasiveness, and limited availability hinder large-scale screening.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>To overcome these barriers, we propose ApneaSense, a non-intrusive IoT-enabled diagnostic framework powered by EvoApneaFormer, an evolutionary transformer-based deep learning model for temporal bio-signal fusion and adaptive learning. EvoApneaFormer combines dynamic self-attention with neuroevolutionary optimization, enabling superior convergence, noise resilience, and generalization across heterogeneous signals. ApneaSense processes synchronized ECG, SpO2, respiratory effort, and motion data on low-power Raspberry Pi 4 hardware &amp;lt;2W using TensorFlow Lite quantization, with a companion Flutter app for real-time visualization, 72-h offline operation, and secure HL7\/FHIR telehealth synchronization. Clinical validation on a hybrid dataset, the UCD Sleep Apnea Database (n = 25) and the ApneaSense Clinical Dataset (n = 61), with stratified patient-level splitting.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>On the held-out test cohort (n = 17), EvoApneaFormer achieved 99.98% accuracy, 99.91% precision, 99.95% recall, a 99.93% F1-score, and a 0.999 macro-AUC. Six apnea classes, namely, normal, obstructive, central, mixed, hypopnea, and respiratory effort-related arousal (RERA), were each identified with \u226599.8% accuracy. Real-world trials confirmed robustness in both clinical 99.8% and home 96.9% settings, even under motion artifacts and signal dropouts. SHAP-based interpretability and uncertainty quantification were deemed actionable by 82% of clinicians.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>Cost-effectiveness analysis indicated break-even in 6.2 months at $5,200\/QALY. Compared to wearable oximeters, ApneaSense suggested a 9.98-point accuracy gain at one-third the cost per test, representing a paradigm shift in edge-based respiratory diagnostics for scalable and personalized sleep apnea monitoring in underserved communities.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fbioe.2026.1755071","type":"journal-article","created":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T05:48:09Z","timestamp":1781156889000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["EvoApneaFormer: an IoT and prognostic evolutionary deep learning-based framework for real-time multi-event sleep apnea disorder detection and remote monitoring"],"prefix":"10.3389","volume":"14","author":[{"family":"Abdullah","sequence":"first","affiliation":[{"name":"Center for Computing Research, Instituto Polit\u00e9cnico Nacional","place":["Mexico City, Mexico"]},{"name":"Department of Computer Science, Bahria University Lahore Campus","place":["Lahore, Pakistan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zulaikha","family":"Fatima","sequence":"additional","affiliation":[{"name":"Department of Allied Health Science, Superior University","place":["Lahore, Pakistan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jos\u00e9 Luis","family":"Oropeza Rodr\u00edguez","sequence":"additional","affiliation":[{"name":"Center for Computing Research, Instituto Polit\u00e9cnico Nacional","place":["Mexico City, Mexico"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Osvaldo Espinosa","family":"Sosa","sequence":"additional","affiliation":[{"name":"Center for Computing Research, Instituto Polit\u00e9cnico Nacional","place":["Mexico City, Mexico"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,6,11]]},"reference":[{"key":"B1","first-page":"780","article-title":"Benefits of sleep","volume":"4","author":"Alimova","year":"2025","journal-title":"Mod. 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