Heart Rate Estimation from PPG Signals at Large Proximal Arteries

Photoplethysmography (PPG) is a noninvasive optical method widely used for cardiovascular monitoring. Its accuracy in heart rate (HR) estimation is challenged by different noise types, especially motion artifacts during physical activity. This study investigates HR estimation from PPG signals recorded at the carotid and brachial arteries before and after ercise, using red (660 nm) and near-infrared (880 nm) light sensors. A pulse-to-pulse time-domain method and a spectral analysis approach were employed. ECG was used as the gold standard. The results demonstrate that the red-light PPG sensor provides superior accuracy, achieving a root mean square error (RMSE) of 2.25 bpm, while spectral analysis exhibited significantly larger errors. When evaluated for heartbeat detection, the proposed approach achieved a sensitivity of 98.45%, a positive predictive value of 99.51%, and a detection error rate of 2.03%, highlighting its robustness in HR estimation. These findings underscore the potential of PPG sensors at large proximal arteries for accurate cardiovascular monitoring and multiparameter assessment, even in the presence of occasional motion artifacts.