Heart rate estimation in photoplethysmogram signals using nonlinear model-based preprocessing

We propose a frequency domain algorithm for heart rate estimation from photoplethysmographic signals (PPG) recorded during intense physical activity. Starting from the Beer-Lambert law we model the effect of movement artifacts on the PPG signal as an amplitude modulation. Using tri-axis accelerometer recordings to demodulate the PPG signal, we are able to extract the heart rate even when motion artifacts are within the frequency band of interest. The average heart rate error using two-channel PPG recordings from 12 subjects was 1:6 BPM with a standard deviation of 3 BPM.