Comparative Analysis of Interbeat Interval and Heart Rate Variability Measurements from Wearable Devices

Heart rate variability (HRV) is a key indicator of autonomic nervous system (ANS) function, playing a crucial role in assessing both cardiovascular and mental health. Traditionally, HRV is measured using electrocardiogram (ECG) devices which provide precise interbeat interval (IBI) detection but can be cumbersome for continuous use. Rapidly growing wearable devices equipped with photoplethysmography (PPG) sensors offer a more convenient and mobile solution for long-term monitoring. In this study, an algorithm is proposed for estimating IBI and HRV from a multichannel PPG-based wearable device, and its accuracy and reliability are evaluated against ECG-based measurements. Performance of the proposed algorithm is compared to that of the device's built-in algorithm. Data were collected from 15 healthy participants engaged in relaxing activities, including lying down, sitting, and walking. The proposed algorithm achieved mean absolute percentage error (MAPE) of 41%, while the device's built-in algorithm exhibited a higher MAPE of 216.6%. These results demonstrate the good accuracy and reliability of the proposed algorithm in estimating HRV. The overall results provide insight into the potential of PPG-based wearables for continuous HRV monitoring.