A supervised learning approach for the robust detection of heart beat in plethysmographic data.

Wearable devices equipped with photoplethysmography (PPG) sensors are gaining an increased interest in the context of biometric signal monitoring within clinical, e-health and fitness settings. When used in everyday life and during exercise, PPG traces are heavily affected by artifacts originating from motion and from a non constant positioning and contact of the PPG sensor with the skin. Many algorithms have been developed for the estimation of heart-rate from photoplethysmography signals. We remark that they were mainly conceived and tested in controlled settings and, in turn, do not provide robust performance, even during moderate exercise. Only a few of them have been designed for signals acquired at rest and during fitness. However, they provide the required resilience to motion artifacts at the cost of using computationally demanding signal processing tools. At variance with other methods from the literature, we propose a supervised learning approach, where a classifier is trained on a set of labelled data to detect the presence of heart beats at each position of a PPG signal, with only little preprocessing and postprocessing. We show that the results obtained on the TROIKA dataset using our approach are comparable with those shown in the original paper, providing a classification error of 14% in the detection of heart beat positions, that reduces to 2.86% on the heart-rate estimates after the postprocessing step.