Heart rate estimation from photoplethysmography signal for wearable health monitoring devices

Abstract

Wearable wrist type health monitoring devices use photoplethysmography (PPG) signal to estimate heart rate (HR). The HR estimation from these devices becomes difficult due to the existence of strong motion artifacts (MA) in PPG signal thereby leading to inaccurate HR estimation. The objective is to develop a novel de-noising algorithm that reduces the MA present in PPG signal, resulting in an accurate HR estimation. A novel de-noising technique using the hierarchical structure of cascade and parallel combinations of two different pairs of adaptive filters which reduces MA from the PPG signal and improves HR estimation is proposed. The first pair combines normalized least mean squares (NLMS) and recursive least squares (RLS) adaptive filters and the second pair combines recursive least squares (RLS) and least mean squares (LMS) adaptive filters. The de-noised signals obtained from the first and second pairs are combined to form a single de-noised PPG signal by means of convex combination. The HR of the de-noised PPG signal is estimated in the frequency domain using a Fast Fourier transform (FFT). Performance of the proposed technique is evaluated using a dataset of 12 individuals performing running activity in Treadmill. It resulted in an average absolute error of 0.92 beats per minute (BPM), standard deviation of the absolute error of 1.17 beats per minute (BPM), average relative error of 0.72 and Pearson correlation coefficient of 0.9973.