Assessing Heart Rate Variability and Pulse Rate Variability Patterns in Cardiac Patients: Exploring the Utility of Photoplethysmography and Electrocardiography

TL;DR: The idea of PRV rather than HRV can be used to assess sympathovagal regulation in a broad community-based investigation alongside HRV under various clinical circumstances.

Abstract: The measure of cardiac variability (HRV) is considered an accurate diagnostic method to identify autonomic dysfunction. The heart rate influences the pulse. Therefore, pulse rate variability (PRV), like HRV, precisely indicates autonomic nervous system tone. The research investigates the correlation between HRV for the ECG signal and PRV in a cardiac patient. Method and Materials: 53 electrocardiogram (ECG) and photoplethysmogram (PPG) signals for 8 minutes from the PhysioNet BIDMC PPG and Respiration Dataset were used. Afterward, the PRV from PPG and the R-R interval calculated independently using Acqknowledge software were used to estimate the ECG and get HRV characteristics. Results: According to the results, there was a strong correlation between the R-R interval for PRV (694.49 ± 112.98ms) and HRV (695.60 ± 112.97ms) (r=0.99). For all linear parameters for both domains, frequency and time of HRV and PRV, there was a significant positive correlation: HFnu (r =0.99), HF Power (r =1), LFnu (r = 0.99), LF Power (r = 1), RMSSD (r = 0.99), SDNN (r = 0.99), NN50 (r = 0.99), and pNN50 (r =0.99). The Bland & Altman plot demonstrated a high level of agreement among approaches for all parameters. Conclusion: According to the finding, which showed a high level of agreement because it provides values similar to PRV and HRV for all parameters, the idea of PRV rather than HRV can be used to assess sympathovagal regulation in a broad community-based investigation alongside HRV under various clinical circumstances.