RMSSD Estimation From Photoplethysmography and Accelerometer Signals Using a Deep Convolutional Network

Christodoulos Kechris, Anastasios Delopoulos

Abstract: Heart Rate Variability is a significant indicator of the Autonomic Neural System’s functioning, traditionally evaluated from electrocardiogram recordings. Photoplethysmography sensors, like electrocardiograph devices, track the heart’s activity and have been widely popularized by their use in smart watches and fitness trackers. In this study we develop a deep learning based approach which is able to successfully estimate the patient’s Root Mean Square of the Successive Differences, a common heart rate variability metric, from lower quality, less expensive photoplethysmography sensors under a wide range of conditions.