RRHR: Embedded Algorithm for Heart Rate and Heart Rate Variability Detection in Wearable Devices, Using Raw ECG Data Between R-R Peaks

Accurate estimation of heart rate (HR) and heart rate variability (HRV) is essential for continuous cardiac monitoring in wearable devices. Most wearable devices use heartbeat sensors that capture Photoplethysmography (PPG) signals to measure HR which can neither identify irregular heartbeats nor arrhythmia-based heart diseases. Cardiac diseases, autonomic nervous system activities, stress levels, and emotional states can be detected using HR calculated from beat to beat or between successive R-R peaks. Few existing studies have calculated HR from RR intervals using timestamps, which vary across systems and are therefore inaccurate. To address this concern, a novel embedded algorithm named “RRHR” is proposed that measures HR and HRV based on raw ECG data between R-R peaks. Compared with an existing method using the MIT-BIH arrhythmia benchmark dataset, the RRHR approach yielded HR values with significantly reduced errors. RRHR was also validated against the existing clinical approach and achieved 100% agreement with the reference heart rate measurements within a tolerance of ±1 ECG sample.