A Dual‐Modal Wearable Pulse Detection System Integrated with Deep Learning for High‐Accuracy and Low‐Power Sleep Apnea Monitoring

Abstract Despite being a serious health condition that significantly increases cardiovascular and metabolic disease risks, sleep apnea syndrome (SAS) remains largely underdiagnosed. While polysomnography (PSG) remains the gold standard for diagnosis, its clinical application is limited by high costs, complex setup requirements, and sleep quality interference. Although wearable devices using photoplethysmography (PPG) have shown promise in SAS detection, their continuous operation demands substantial power consumption, hindering long‐term monitoring capabilities. Here, a dual‐modal wearable system is presented integrating a piezoelectric nanogenerator (PENG) and PPG sensor with a biomimetic fingertip structure for SAS detection. A two‐stage detection strategy is adopted where the self‐powered PENG performs continuous preliminary screening, activating the PPG sensor only when suspicious events are detected. Combined with a Vision Transformer‐based deep learning model, the high‐accuracy configuration achieves 99.59% accuracy, while the low‐power two‐stage approach maintained 94.95% accuracy. This dual‐modal wearable pulse detection system provides a practical solution for long‐term SAS monitoring, overcoming the limitations of traditional PSG while maintaining high detection accuracy. The system's versatility in both home and clinical settings offers the potential for improving early detection rates and treatment outcomes for SAS patients.