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

Abstract

Objectives: Remote 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 rPPGand PPG-derived UST-PRV metrics was studied. Approach: Three 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 results: The 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. Significance: Remote 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.

Performance Evaluation of rPPG Approaches with and without the Region-of-Interest Localization Step

“…In the case of calculating RMSE, the length of the processing window plays an important role when interpreting the results (differences of up to 10% may occur for the window lengths from 0 to 60 s [41]). The described issue results from the already exposed drawback of the rPPG research, i.e., the lack of standardized methodology [42]. There were even some attempts to define standardized report procedures for assessing rPPG PR measurements [43] with the goal to ensure direct comparison of the results of different studies, but there seems to be no general agreement on this issue.…”

Efficient Spatiotemporal Attention Network for Remote Heart Rate Variability Analysis

“…The authors of this study used their own private dataset for HRV metrics evaluation. Finžgar and Podržaj in [ 17 ] proposed a method to recover rPPG signal through wavelet transform and custom beat interval filtering. They proved good correlation between time domain ultra-short term HRV measurements from rPPG and PPG through a series of experiments and evaluated it on the public dataset PURE [ 18 ].…”

An Open Framework for Remote-PPG Methods and Their Assessment

“…However it has also been pointed out how the compression can destroy and pollute the subtle pulsatile information essential to rPPG. In [56] it has been claimed that uncompressed videos could increase SNR due to information being lost during the video compression process. Similarly, in [29] a more in depth analysis has been conducted, aiming at finding an acceptable level of compression, indeed necessary in real world applications.…”