Optimisation of artefact detection in photoplethysmography heart rate data: influence of different classifiers in machine learning models

Abstract

Background: Heart rate (HR) tracking by wrist-worn devices using photoplethysmography (PPG) could assist in continuously following up physical activity. However, the accuracy can be impacted by (motion) artefacts. Machine learning models could help to recognise artefacts in PPG-based HR data. The choice of classifier in these machine learning models is a determing factor for task performance of the model.