Abstract
Alterations of heart rate variability (HRV) are associated with various (patho)physiological conditions; therefore, HRV analysis has the potential to become a useful diagnostic module of wearable/telemedical devices to support remote cardiovascular/autonomic monitoring. Continuous pulse recordings obtained by photoplethysmography (PPG) can yield pulse rate variability (PRV) indices similar to HRV parameters; however, it is debated whether PRV/HRV parameters are interchangeable. In this study, we assessed the PRV analysis module of a digital arterial PPG-based telemedical system (SCN4ALL). We used Bland–Altman analysis to validate the SCN4ALL PRV algorithm to Kubios Premium software and to determine the agreements between PRV/HRV results calculated from 2-min long PPG and ECG captures recorded simultaneously in healthy individuals (n = 33) at rest and during the cold pressor test, and in diabetic patients (n = 12) at rest. We found an ideal agreement between SCN4ALL and Kubios outputs (bias < 2%). PRV and HRV parameters showed good agreements for interbeat intervals, SDNN, and RMSSD time-domain variables, for total spectral and low-frequency power (LF) frequency-domain variables, and for non-linear parameters in healthy subjects at rest and during cold pressor challenge. In diabetics, good agreements were observed for SDNN, LF, and SD2; and moderate agreement was observed for total power. In conclusion, the SCN4ALL PRV analysis module is a good alternative for HRV analysis for numerous conventional HRV parameters.
Highlights
The time duration between heart beats continuously changes, even at rest
There is no significant difference in mean interbeat intervals defined from PPG and ECG captures, and for several heart rate variability (HRV)/pulse rate variability (PRV) parameters computed by the Kubios software, the limits of agreement are within 10% (i.e., SDNN and RMSSD among time-domain variables, total power and LF frequency-domain indices, and non-linear parameters)
Our study showed that the HRV algorithm of the SCN4ALL system is as accurate as the widely used Kubios HRV Premium algorithm for PRV analysis of short (2-min long) time series of interbeat intervals obtained by PPG recordings
Summary
The time duration between heart beats (interbeat intervals, IBIs) continuously changes, even at rest These alterations are referred to as heart rate variability (HRV) and are brought about by various oscillating regulatory mechanisms that directly or indirectly affect heart rate (HR). These processes dominantly act by modifying the balance of sympathetic and parasympathetic effects on the heart; HR fluctuations due to other regulatory mechanisms (chemical, hormonal, and hemodynamic factors) participate [1,2,3,4]. HRV analysis provides indices that characterize the variability of the IBIs (time-domain parameters) [2] and that reflect the contribution of control mechanisms oscillating at different frequencies to this variability (frequency-domain parameters). Socalled non-linear parameters that characterize the unpredictability of HR are derived
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