Biomedical Research

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Statistical analysis of pulse rate variability quantified through second derivative photoplethysmogram (SDPPG) and its compatibility with electrocardiographic (ECG) heart rate variability

Heart Rate Variability (HRV) and Pulse Rate Variability (PRV) are non-invasive techniques to assess the function of autonomic nervous system and presently, PRV is considered to be a good alternative to HRV. We investigated a different approach to potentially show that the second derivative of Photoplethysmography signals (SDPPG) can measure PRV with accurate detection of “a” waves through re-sampling technique which normalizes the signal and ensures the presence of “a” waves in all its recurrences. PPG and ECG signals of 28 records, each of 10 second duration that varies from healthy adults to unhealthy and aged patients with different morphologies such as regular and irregular heart rhythms, low and varying amplitudes, obtained from the large-scale openly available database from PhysioNet were employed for the performance evaluation and validation. The performance of “a” wave detection algorithm resulted in 99.75% sensitivity and 100% positive predictivity. In this study, a-a intervals of SDPPG signals and R-R intervals of ECG signals were used to determine PRV and HRV indices such as the standard deviation of heart beat interval (SDNN) and the root-mean square of the difference of successive heart beats (rMSSD). HRV and PRV indices thus obtained from ECG and SDPPG were compared by linear regression analysis. Highly significant correlations were found between PRV indices(r=0.902207) estimated through SDPPG measures and the corresponding HRV indices (r=0.825032), substantiating that PRV can be an alternative for HRV.

Author(s): Mohanalakshmi S, Sivasubramanian A, Swarnalatha A