Heart Rate Monitoring using Sparse Spectral Curve Tracing

Heart rate (HR) monitoring under real-world activities of daily living conditions is challenging, particularly, using peripheral wearable devices integrated with simple optical and acceleration sensors. The study presents a novel technique, named as CurToSS: CURve Tracing On Sparse Spectrum, for continuous HR estimation in daily living activity conditions using simultaneous photoplethysmogram (PPG) and triaxial-acceleration signals. The performance validation of HR estimation using the CurToSS algorithm is conducted in four public databases with distinctive study groups, sensor types, and protocols involving intense physical and emotional exertions. The HR performance of this time-frequency curve tracing method is also compared to that of contemporary algorithms. The results suggest that the CurToSS method offers the best performance with significantly (P<0.01) lowest HR error compared to spectral filtering and multi-channel PPG correlation methods. The current HR performances are also consistently better than a deep learning approach in diverse datasets. The proposed algorithm is powerful for reliable long-term HR monitoring under ambulatory daily life conditions using wearable biosensor devices.