ArticleFrontiers in bioengineering and biotechnology2026
EvoApneaFormer: an IoT and prognostic evolutionary deep learning-based framework for real-time multi-event sleep apnea disorder detection and remote monitoring.
Article in Frontiers in bioengineering and biotechnology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Introduction: 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 > $6,000 per unit, invasiveness, and limited availability hinder large-scale screening. Methods: 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 <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. Results: 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 ≥99.8% 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. Discussion: 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.
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