Analysis of heart rate variability using mobile devices and machine learning

Abstract

This paper presents methods of heart rate variability analysis based on electrocardiographic and photoplethysmographic signals obtained using mobile devices. An approach to processing the recorded data involving noise suppression, peak detection, and determining inter-pulse intervals in the physiological signal was developed. Detection of R peaks in ECG and maxima of a PPG wave was performed using convolutional and recurrent neural networks. The accuracy of the models was measured in comparison to the classic algorithms and reference data. The results show a high accuracy of peak identification and allow employing mobile devices to monitor cardiac parameters in both home environments and during physical activities.