Multimodal Assessment of the Pulse Rate Variability Analysis Module of a Photoplethysmography-Based Telemedicine System

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.

Continuous monitoring with wearables in multiple sclerosis reveals an association of cardiac autonomic dysfunction with disease severity

“…Given the more reliable normative data available for nonlinear-domain metrics and their superior reliability in PPG measurements, using SD1% and SD2% is the better choice. 27 …”

“…and Antali et al ., most metrics are omitted due to redundancy or due to evidence of them being prone to artifacts, or artificial noise, leaving SDNN, SD1, and SD2 as metrics for evaluation. 26 , 27 …”

“…25 According to Ciccone et al and Antali et al, most metrics are omitted due to redundancy or due to evidence of them being prone to artifacts, or artificial noise, leaving SDNN, SD1, and SD2 as metrics for evaluation. 26,27 In a recent publication, Natarajan et al presented normative values for PPG sensor-based HRV features of more than 8 million Fitbit® users. 28 We divided the computed metrics by the provided age, sex, and time-of-day specific normative values, obtaining percentage scores.. 28,29 These represent the percentage deviation of the measured value from the reference, mitigating the influence of age, sex, and time-of-day differences in group comparisons (also see Figure S2).…”

Cardiodiagnostics Based on Photoplethysmographic Signals

“…Heart rate variability, determined based on the use of PPG signals, is a relatively little studied method, and at the same time, PPG sensors are convincingly entering the everyday life of modern people. The authors of [ 18 ] studied PPG records of patients diagnosed with diabetes using HRV parameters in the time and frequency range. Publication [ 19 ] examined healthy young and adult individuals using HRV analysis of PPG signals.…”

Estimating Resting HRV during fMRI: A Comparison between Laboratory and Scanner Environment

“…Most research groups that acquire cardiac signals in brain imaging studies use the finger pulse instead of ECGs, as revealed by a mega-analysis of neuroimaging data from an international community [8]. Several studies suggested that the pulse rate can be used as an accurate surrogate for the heart rate [21][22][23]. A meta-analysis by Schäfer and Vagedes (2013) concluded on the 'sufficient accuracy' of HRV derived from the finger pulse, especially during the resting condition [24].…”