FECT-OSA: A transformer-enhanced multimodal system for non-contact sleep apnea monitoring

Obstructive sleep apnea (OSA) is a common sleep disorder linked to an increased risk of cardiovascular and neurocognitive disorders. While portable wearables provide a low-cost alternative for OSA detection compared to polysomnography (PSG), their prolonged wear burden, limited accuracy, and susceptibility to motion artifacts hinder practical application. This study introduces FECT-OSA, a non-contact framework that extracts reliable OSA indicators from piezoelectric physiological signals (PPS). FECT-OSA combines three key components: adaptive signal processing, TransUnet, and ConvTransLSTM. Adaptive signal processing utilizes entropy-based selection to determine the most stable input, improving signal robustness. TransUnet improves feature extraction by integrating U-Net’s localization with Transformer-based global modeling. It segments critical spectrogram regions, mitigating motion artifacts and sidelobe interference. ConvTransLSTM integrates CNNs for local feature extraction, a Multimodal Transformer for global alignment, and LSTM for temporal modeling, enhancing OSA detection accuracy. In a study of 34 patients, FECT-OSA achieved 84.79% accuracy, 74.80% sensitivity, and 88.53% specificity, surpassing state-of-the-art portable devices. For severe OSA patients ( A H I ≥ 30 ) , it attains 94.1% accuracy and 90.9% sensitivity/specificity, exceeding traditional methods by 5%–15% in accuracy and 5%–22% in sensitivity. FECT-OSA’s non-contact design minimizes patient discomfort while ensuring high accuracy across varying OSA severities, providing a practical home-based alternative.