Detecting stress from imaging photoplethysmography using high frame rate video and a yellow-green filter: A pilot study

2020 ◽  
Vol 61 ◽  
pp. C273-C287
Author(s):  
Peter Vincent Aquilina ◽  
David Booth ◽  
Brandon Pincombe ◽  
Gary Hanly ◽  
Kym Meaney ◽  
...  
Keyword(s):  
Pilot Study ◽  
Pulse Rate ◽  
International Society ◽  
Spline Interpolation ◽  
Frame Rate ◽  
Peak Time ◽  
Pulse Rate Variability ◽  
High Frame Rate ◽  
Green Filter ◽  
Stress Levels

We investigate the use of a yellow-green filter to increase the signal-to-noise ratio (snr) in imaging photoplethysmography (iPPG) and test if high frame rate (HFR) video improves the accuracy of the derived heart rate variability (HRV). This pilot study is associated with a broader program to use iPPG to detect and monitor stress levels using HRV. To improve the snr of the iPPG signal, we employ two HFR colour video cameras of which one was fitted with a yellow-green filter (corresponding to the haemoglobin absorption peak within the visible spectrum). To our knowledge, the benefit of a yellow-green filter has never been explored. The predominant influence on HRV comes from the autonomic nervous system (ANS), which connects directly to the heart and cues the human body to relax or to stress. The linkage of HRV to the ANS makes HRV a proxy for stress levels. The HRV is derived from the iPPG signal by first using a cubic spline interpolation for more precise peak detection, and then calculating the inter-beat intervals from the peak-to-peak time differences. Instead of interpolating the signal, we hypothesise that a more accurate HRV measurement can be obtained using a HFR video camera, in our case at 200 frames per second. References E. B. Blackford, J. R. Estepp, and D. J. McDuff. Remote spectral measurements of the blood volume pulse with applications for imaging photoplethysmography. In G. L. Cote, editor, Optical Diagnostics and Sensing XVIII: Toward Point-of-Care Diagnostics, volume 10501, page 105010Z. International Society for Optics and Photonics, SPIE, 2018. doi:10.1117/12.2291073. M. Brayne. Trauma and Journalism: A Guide For Journalists, Editors and Managers. DART Center for Journalism and Trauma, 2007. https://dartcenter.org/sites/default/files/DCE_JournoTraumaHandbook.pdf. L. F. C. Martinez, G. Paez, and M. Strojnik. Optimal wavelength selection for noncontact reflection photoplethysmography. In Proceedings of the 22nd Congress of the International Commission for Optics: Light for the Development of the World, volume 8011, page 801191. International Society for Optics and Photonics, SPIE, 2011. doi:10.1117/12.903190. Y. Sun, S. Hu, V. Azorin-Peris, R. Kalawsky, and S. E. Greenwald. Noncontact imaging photoplethysmography to effectively access pulse rate variability. J. Biomed. Optics, 18(6):061205, 2013. doi:10.1117/1.JBO.18.6.061205. A. M. Unakafov. Pulse rate estimation using imaging photoplethysmography: generic framework and comparison of methods on a publicly available dataset. Biomed. Phys. Eng. Exp., 4(4):045001, 2018. doi:10.1088/2057-1976/aabd09.

Influence of acquisition frame-rate and video compression techniques on pulse-rate variability estimation from vPPG signal

2017 ◽  
Vol 0 (0) ◽  
Author(s):  
Luca Cerina ◽  
Luca Iozzia ◽  
Luca Mainardi
Keyword(s):  
Frequency Domain ◽  
Video Compression ◽  
Pulse Rate ◽  
Parameters Estimation ◽  
Frame Rate ◽  
Signal Extraction ◽  
Measured Signal ◽  
Pulse Rate Variability ◽  
Time Jitter ◽  
Video Recordings

AbstractIn this paper, common time- and frequency-domain variability indexes obtained by pulse rate variability (PRV) series extracted from video-photoplethysmographic signal (vPPG) were compared with heart rate variability (HRV) parameters calculated from synchronized ECG signals. The dual focus of this study was to analyze the effect of different video acquisition frame-rates starting from 60 frames-per-second (fps) down to 7.5 fps and different video compression techniques using both lossless and lossy codecs on PRV parameters estimation. Video recordings were acquired through an off-the-shelf GigE Sony XCG-C30C camera on 60 young, healthy subjects (age 23±4 years) in the supine position. A fully automated, signal extraction method based on the Kanade-Lucas-Tomasi (KLT) algorithm for regions of interest (ROI) detection and tracking, in combination with a zero-phase principal component analysis (ZCA) signal separation technique was employed to convert the video frames sequence to a pulsatile signal. The frame-rate degradation was simulated on video recordings by directly sub-sampling the ROI tracking and signal extraction modules, to correctly mimic videos recorded at a lower speed. The compression of the videos was configured to avoid any frame rejection caused by codec quality leveling, FFV1 codec was used for lossless compression and H.264 with variable quality parameter as lossy codec. The results showed that a reduced frame-rate leads to inaccurate tracking of ROIs, increased time-jitter in the signals dynamics and local peak displacements, which degrades the performances in all the PRV parameters. The root mean square of successive differences (RMSSD) and the proportion of successive differences greater than 50 ms (PNN50) indexes in time-domain and the low frequency (LF) and high frequency (HF) power in frequency domain were the parameters which highly degraded with frame-rate reduction. Such a degradation can be partially mitigated by up-sampling the measured signal at a higher frequency (namely 60 Hz). Concerning the video compression, the results showed that compression techniques are suitable for the storage of vPPG recordings, although lossless or intra-frame compression are to be preferred over inter-frame compression methods. FFV1 performances are very close to the uncompressed (UNC) version with less than 45% disk size. H.264 showed a degradation of the PRV estimation directly correlated with the increase of the compression ratio.

PO-1736: High frame rate CAIPIRINHA-TWIST-VIBE MRI for volumetric motion monitoring in MRgRT: a pilot study

2020 ◽  
Vol 152 ◽  
pp. S962-S963
Author(s):  
J. Yuan ◽  
O.L. Wong ◽  
R.Y.W. Ho ◽  
Y. Zhou ◽  
K.Y. Cheung ◽  
...  
Keyword(s):  
Pilot Study ◽  
Frame Rate ◽  
High Frame Rate

Measurement of regional pulse wave velocity using very high frame rate ultrasound

Choonpa Igaku ◽  
2015 ◽  
Vol 42 (6) ◽  
pp. 701-709
Author(s):  
Hideyuki HASEGAWA ◽  
Kazue HONGO ◽  
Hiroshi KANAI
Keyword(s):  
Pulse Wave Velocity ◽  
Wave Velocity ◽  
Pulse Wave ◽  
Frame Rate ◽  
High Frame Rate ◽  
Very High

scholarly journals High frame-rate multichannel beam-scanning microscopy based on Lissajous trajectories

2014 ◽  
Vol 22 (20) ◽  
pp. 24224 ◽  
Author(s):  
Shane Z. Sullivan ◽  
Ryan D. Muir ◽  
Justin A. Newman ◽  
Mark S. Carlsen ◽  
Suhas Sreehari ◽  
...  
Keyword(s):  
Frame Rate ◽  
Scanning Microscopy ◽  
Beam Scanning ◽  
High Frame Rate ◽  
Lissajous Trajectories

scholarly journals Limits of subjective and objective vection for ultra-high frame rate visual displays

Displays ◽  
2020 ◽  
Vol 64 ◽  
pp. 101961 ◽  
Author(s):  
Séamas Weech ◽  
Sophie Kenny ◽  
Claudia Martin Calderon ◽  
Michael Barnett-Cowan
Keyword(s):  
Frame Rate ◽  
Visual Displays ◽  
High Frame Rate

scholarly journals Differential effects of the blood pressure state on pulse rate variability and heart rate variability in critically ill patients

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Elisa Mejía-Mejía ◽  
James M. May ◽  
Mohamed Elgendi ◽  
Panayiotis A. Kyriacou
Keyword(s):  
Blood Pressure ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Pulse Rate ◽  
Pulse Transit Time ◽  
Pulse Rate Variability ◽  
Physiological Factors ◽  
Ecg Signals ◽  
Non Invasive ◽  
Electrocardiogram Ecg

AbstractHeart rate variability (HRV) utilizes the electrocardiogram (ECG) and has been widely studied as a non-invasive indicator of cardiac autonomic activity. Pulse rate variability (PRV) utilizes photoplethysmography (PPG) and recently has been used as a surrogate for HRV. Several studies have found that PRV is not entirely valid as an estimation of HRV and that several physiological factors, including the pulse transit time (PTT) and blood pressure (BP) changes, may affect PRV differently than HRV. This study aimed to assess the relationship between PRV and HRV under different BP states: hypotension, normotension, and hypertension. Using the MIMIC III database, 5 min segments of PPG and ECG signals were used to extract PRV and HRV, respectively. Several time-domain, frequency-domain, and nonlinear indices were obtained from these signals. Bland–Altman analysis, correlation analysis, and Friedman rank sum tests were used to compare HRV and PRV in each state, and PRV and HRV indices were compared among BP states using Kruskal–Wallis tests. The findings indicated that there were differences between PRV and HRV, especially in short-term and nonlinear indices, and although PRV and HRV were altered in a similar manner when there was a change in BP, PRV seemed to be more sensitive to these changes.

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