Dataset Information

Video-Based Pulse Rate Variability Measurement Using Periodic Variance Maximization and Adaptive Two-Window Peak Detection.

ABSTRACT: Many previous studies have shown that the remote photoplethysmography (rPPG) can measure the Heart Rate (HR) signal with very high accuracy. The remote measurement of the Pulse Rate Variability (PRV) signal is also possible, but this is much more complicated because it is then necessary to detect the peaks on the temporal rPPG signal, which is usually quite noisy and has a lower temporal resolution than PPG signals obtained by contact equipment. Since the PRV signal is vital for various applications such as remote recognition of stress and emotion, the improvement of PRV measurement by rPPG is a critical task. Contact based PRV measurement has already been investigated, but the research on remotely measured PRV is very limited. In this paper, we propose to use the Periodic Variance Maximization (PVM) method to extract the rPPG signal and event-related Two-Window algorithm to improve the peak detection for PRV measurement. We have made several contributions. Firstly, we show that the newly proposed PVM method and Two-Window algorithm can be used for PRV measurement in the non-contact scenario. Secondly, we propose a method to adaptively determine the parameters of the Two-Window method. Thirdly, we compare the algorithm with other attempts for improving the non-contact PRV measurement such as the Slope Sum Function (SSF) method and the Local Maximum method. We calculated several features and compared the accuracy based on the ground truth provided by contact equipment. Our experiments showed that this algorithm performed the best of all the algorithms.

SUBMITTER: Li P

PROVIDER: S-EPMC7294433 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Video-Based Pulse Rate Variability Measurement Using Periodic Variance Maximization and Adaptive Two-Window Peak Detection.

Li Peixi P Benezeth Yannick Y Macwan Richard R Nakamura Keisuke K Gomez Randy R Li Chao C Yang Fan F

Sensors (Basel, Switzerland) 20200512 10

Many previous studies have shown that the remote photoplethysmography (rPPG) can measure the Heart Rate (HR) signal with very high accuracy. The remote measurement of the Pulse Rate Variability (PRV) signal is also possible, but this is much more complicated because it is then necessary to detect the peaks on the temporal rPPG signal, which is usually quite noisy and has a lower temporal resolution than PPG signals obtained by contact equipment. Since the PRV signal is vital for various applicat ...[more]

PMID: 32408526

Similar Datasets

Project description: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 rPPG- and 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.

Project description:PurposeTo assess intraobserver, interobserver, and scan-rescan variability of MRI aortic stiffness measurements in a multicenter trial setting.Materials and methodsThis study was a retrospective analysis of prospectively collected data in a multicenter prospective clinical trial (clinicaltrials.gov ID NCT01870739). Forty-five adult patients (31 men; mean age, 58 years ± 12 [standard deviation]; 15 patients per center; three centers) with arterial hypertension underwent standardized 3-T baseline MRI assessments between June and September 2014. Aortic strain was calculated from maximum and minimum aortic area measurements repeated three times by three readers at three aortic levels on three retrospectively gated axial gradient-echo (GRE) data sets. Pulse wave velocity (PWV) was assessed three times by five readers as Δx/Δt: Δx was measured on a parasagittal GRE image of the aortic arch, and Δt was extracted from ascending and descending aortic velocity curves created on three axial phase-contrast acquisitions. Intraobserver, interobserver, and scan-rescan variability was calculated using percentage coefficient of variation (COV).ResultsAortic strain variability was lowest at the level of the distal descending aorta (DDA) with median COVs of 1.6% for intraobserver variability, 4.0% for interobserver variability, and 10.3% for scan-rescan variability. It was highest at the ascending aorta (AA) with COVs of 3.6% for intraobserver variability, 10.7% for interobserver variability, and 19.8% for scan-rescan variability. Variability of PWV was low: 0.7% for intraobserver variability, 1.5% for interobserver variability, and 8.1% for scan-rescan variability.ConclusionLow variability can be achieved for aortic strain and PWV measurements in a multicenter trial setting using standardized MRI protocols. Although COV was lower when measuring aortic strain at DDA compared with AA, variability was acceptable at both anatomic locations.Supplemental material is available for this article.© RSNA, 2020.

Dataset Information

Video-Based Pulse Rate Variability Measurement Using Periodic Variance Maximization and Adaptive Two-Window Peak Detection.

Publications

Video-Based Pulse Rate Variability Measurement Using Periodic Variance Maximization and Adaptive Two-Window Peak Detection.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets