Exploring the Feasibility of PPG for Estimation of Heart Rate Variability: A Mathematical Approach

Smart monitoring of cardiovascular diseases (CVDs) is gaining popularity in the healthcare field due to a wide range of applications using electrocardiogram (ECG) and photoplethysmogram (PPG) based techniques. ECG-based estimation and analysis of Heart Rate Variability (HRV) is a prominent technique used to assess cardiovascular health. However, it is expensive and has little practical application for daily monitoring of HRV. To solve such an issue, we propose a mathematical model based on statistical measures using a based technique. The model pre-processes pulse sensor data to mitigate specific noise or artifacts using filtering and statistical signal processing techniques. The model performs statistical measures to calculate heart rate from the interbeat interval (IBI) between consecutive R-peaks of the pre-processed data. Moreover, the proposed model used time domain techniques to estimate HRV values using metrics including standard deviation of NN (SDNN) and root mean square of successive differences (RMSSD) for the proposed model and succeeded in achieving an accuracy of 89.19% for HRV and 99.89% for HR, which was then developed into a prototype for evaluating the accuracy of the proposed model.