Feasibility of assessing ultra-short-term pulse rate variability from video recordings.

Miha Finžgar,Primož Podržaj

doi:10.7717/peerj.8342

Abstract

ObjectivesRemote photoplethysmography (rPPG) is a promising non-contact measurement technique for assessing numerous physiological parameters: pulse rate, pulse rate variability (PRV), respiratory rate, pulse wave velocity, blood saturation, blood pressure, etc. To justify its use in ultra-short-term (UST) PRV analysis, which is of great benefit for several healthcare applications, the agreement between rPPG- and PPG-derived UST-PRV metrics was studied.ApproachThree time-domain metrics—standard deviation of normal-to-normal (NN) intervals (SDNN), root mean square of successive NN interval differences (RMSSD), and the percentage of adjacent NN intervals that differ from each other by more than 50 ms (pNN50)—were extracted from 56 video recordings in a publicly available data set. The selected metrics were calculated on the basis of three groups of 10 s recordings and their average, two groups of 30 s recordings and their average, and a group of 60 s recordings taken from the full-length recordings and then compared with metrics derived from the corresponding reference (PPG) pulse waveform signals by using correlation and effect size parameters, and Bland–Altman plots.Main resultsThe results show there is stronger agreement as the recording length increases for SDNN and RMSSD, yet there is no significant change for pNN50. The agreement parameters reach r = 0.841 (p < 0.001), r = 0.529 (p < 0.001), and r = 0.657 (p < 0.001), estimated median bias −1.52, −2.28 ms and −1.95% and a small effect size for SDNN, RMSSD, and pNN50 derived from the 60 s recordings, respectively.SignificanceRemote photoplethysmography-derived UST-PRV metrics manage to capture UST-PRV metrics derived from reference (PPG) recordings well. This feature is highly desirable in numerous applications for the assessment of one’s health and well-being. In future research, the validity of rPPG-derived UST-PRV metrics compared to the gold standard electrocardiography recordings is to be assessed.

Highlights

Heart-rate variability (HRV) concerns the fluctuations of interbeat intervals (IBIs), that is, the time intervals between two consecutive successive heartbeats (Shaffer & Ginsberg, 2017)
We first tested if any differences existed between the six different recording setups in terms of mean SDNN, RMSSD, and pNN50 derived from the 60 s reference (PPG) recordings
The results indicate that none of the setups elicited a change in the physiological state of the subject that would be reflected in a significant change in SDNN, RMSSD, or pNN50

Summary

Introduction

Heart-rate variability (HRV) concerns the fluctuations of interbeat intervals (IBIs), that is, the time intervals between two consecutive successive heartbeats (Shaffer & Ginsberg, 2017). It largely depends on regulation by the autonomic nervous system (ANS) of. The level of HRV gives information about the ANS’ functionality and the heart’s ability to respond to it (Rajendra Acharya et al, 2006). The HRV standard defines long-term (LT; 24 h) and short-term (ST; 5 min) HRV assessment by means of time-domain, frequency-domain, and non-linear metrics (Malik et al, 1996).

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