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Biomedical Engineering / Biomedizinische Technik

Joint Journal of the German Society for Biomedical Engineering in VDE and the Austrian and Swiss Societies for Biomedical Engineering and the German Society of Biomaterials

Editor-in-Chief: Dössel, Olaf

Editorial Board: Augat, Peter / Habibović, Pamela / Haueisen, Jens / Jahnen-Dechent, Wilhelm / Jockenhoevel, Stefan / Knaup-Gregori, Petra / Lenarz, Thomas / Leonhardt, Steffen / Plank, Gernot / Radermacher, Klaus M. / Schkommodau, Erik / Stieglitz, Thomas / Boenick, Ulrich / Jaramaz, Branislav / Kraft, Marc / Lenthe, Harry / Lo, Benny / Mainardi, Luca / Micera, Silvestro / Penzel, Thomas / Robitzki, Andrea A. / Schaeffter, Tobias / Snedeker, Jess G. / Sörnmo, Leif / Sugano, Nobuhiko / Werner, Jürgen /


IMPACT FACTOR 2017: 1.096
5-year IMPACT FACTOR: 1.492

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1862-278X
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Volume 64, Issue 1

Issues

Volume 57 (2012)

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

Luca CerinaORCID iD: https://orcid.org/0000-0002-4166-2110 / Luca Iozzia
  • Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano, Italy
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Luca Mainardi
  • Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano, Italy
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
Published Online: 2017-11-14 | DOI: https://doi.org/10.1515/bmt-2016-0234

Abstract

In 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.

Keywords: camera-based PPG; distance PPG; heart-rate variability; imaging PPG; non-contact PPG; non-contact pulse-rate variability; remote PPG; video compression; video frame-rate; video-photoplethysmography

References

  • [1]

    Amelard R, Scharfenberger C, Kazemzadeh F, et al. Feasibility of long-distance heart rate monitoring using transmittance photoplethysmographic imaging (PPGI). Sci Rep 2015; 5.Web of ScienceGoogle Scholar

  • [2]

    Bal U. Non-contact estimation of heart rate and oxygen saturation using ambient light. Biomed Opt Express 2015; 6: 86–97.PubMedCrossrefWeb of ScienceGoogle Scholar

  • [3]

    Billman GE. The LF/HF ratio does not accurately measure cardiac sympatho-vagal balance. Front Physiol 2013; 4: 26.Web of SciencePubMedGoogle Scholar

  • [4]

    Blackford EB, Estepp JR. Effects of frame rate and image resolution on pulse rate measured using multiple camera imaging photoplethysmography. In: SPIE Medical Imaging. International Society for Optics and Photonics 2015: 14–28.Google Scholar

  • [5]

    Blanik N, Venema B, Blazek V, Leonhardt S. Remote pulse oximetry imaging: fundamentals and applications. Lék Tech 2014; 44: 5–11.Google Scholar

  • [6]

    Bousefsaf F, Maaoui C, Pruski A. Remote detection of mental workload changes using cardiac parameters assessed with a low-cost webcam. Comput Biol Med 2014; 53: 154–163.PubMedWeb of ScienceCrossrefGoogle Scholar

  • [7]

    Chung A, Wang XY, Amelard R, et al. High-resolution motion-compensated imaging photoplethysmography for remote heart rate monitoring. In: SPIE BiOS. International Society for Optics and Photonics 2015: 160–165.Google Scholar

  • [8]

    Chwyl B, Chung AG, Amelard R, Deglint J, Clausi DA, Wong A. Sapphire: stochastically acquired photoplethysmogram for heart rate inference in realistic environments. In: 2016 IEEE International Conference on Image Processing (ICIP), Phoenix, Arizona, USA. 2016: 1230–1234.Google Scholar

  • [9]

    Cui Y, Fu CH, Hong H, Zhang Y, Shu F. Non-contact time varying heart rate monitoring in exercise by video camera. In: 2015 International Conference on Wireless Communications Signal Processing (WCSP), Nanjing, Jiangsu, China. 2015: 1–5.Google Scholar

  • [10]

    de Haan G, Jeanne V. Robust pulse rate from chrominance-based RPPG. IEEE Trans Biomed Eng 2013; 60: 2878–2886.Web of ScienceCrossrefPubMedGoogle Scholar

  • [11]

    Fitzpatrick TB. The validity and practicality of sun-reactive skin types I through VI. Arch Dermatol 1988; 124: 869–871.PubMedCrossrefGoogle Scholar

  • [12]

    Fonseca D, Netto A, Ferreira R, de Sá AM. Lomb-scargle periodogram applied to heart rate variability study. In: 2013 ISSNIP Biosignals and Biorobotics Conference (BRC), Rio de Janerio, Brazil. IEEE 2013: 1–4.Google Scholar

  • [13]

    Ghutke RC, Naveen C, Satpute VR. A novel approach for video frame interpolation using cubic motion compensation technique. International Journal of Applied Engineering Research 2016; 11: 7139–7146.Google Scholar

  • [14]

    Gunther J, Ruben N, Moon T. Model-based (passive) heart rate estimation using remote video recording of moving human subjects illuminated by ambient light. In: IEEE International Conference on Image Processing (ICIP). Quebec city, QC. 2015: 2870–2874.Google Scholar

  • [15]

    Hanfland S. Video format dependency of PPGI signals. In: 20th International Student Conference on Electrical Engineering. 2016.Google Scholar

  • [16]

    Huang RY, Dung LR. A motion-robust contactless photoplethysmography using chrominance and adaptive filtering. In: Biomedical Circuits and Systems Conference (BioCAS), Atlanta, Georgia, USA. IEEE, 2015: 1–4.Google Scholar

  • [17]

    Huelsbusch M, Blazek V. Contactless mapping of rhythmical phenomena in tissue perfusion using PPGI. IOP Med Imag J 2002: 110–117.Google Scholar

  • [18]

    Humphreys K, Ward T, Markham C. Noncontact simultaneous dual wavelength photoplethysmography: a further step toward noncontact pulse oximetry. Rev Sci Instrum 2007; 78: 044304.CrossrefPubMedWeb of ScienceGoogle Scholar

  • [19]

    Iozzia L, Cerina L, Mainardi L. Relationships between heart-rate variability and pulse-rate variability obtained from video-PPG signal using ZCA. Physiol Meas 2016; 37: 1934.Web of ScienceCrossrefPubMedGoogle Scholar

  • [20]

    Kumar M, Veeraraghavan A, Sabharwal A. Distanceppg: robust non-contact vital signs monitoring using a camera. Biomed Opt Express 2015; 6: 1565–1588.Web of ScienceCrossrefPubMedGoogle Scholar

  • [21]

    Kwon S, Kim J, Lee D, Park K. Roi analysis for remote photoplethysmography on facial video. In: 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Milano, Italy. 2015: 4938–4941.Google Scholar

  • [22]

    Lewandowska M, Rumiński J, Kocejko T, Nowak J. Measuring pulse rate with a webcam – a non-contact method for evaluating cardiac activity. In: 2011 Federated Conference on Computer Science and Information Systems (FedCSIS), Szczecin, Poland. IEEE, 2011: 405–410.Google Scholar

  • [23]

    Li X, Chen J, Zhao G, Pietikainen M. Remote heart rate measurement from face videos under realistic situations. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2014: 4264–4271.Google Scholar

  • [24]

    Lucas BD, Kanade T. Detection and tracking of point features. Technical Report MU-CS-91-132, Carnegie Mellon University 1991.Google Scholar

  • [25]

    McDuff D, Gontarek S, Picard RW. Improvements in remote cardiopulmonary measurement using a five band digital camera. IEEE Trans Biomed Eng 2014; 61: 2593–2601.Web of ScienceCrossrefPubMedGoogle Scholar

  • [26]

    McDuff DJ, Blackford EB, Estepp JR. The impact of video compression on remote cardiac pulse measurement using imaging photoplethysmography. In: 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017), Washington D.C., USA. IEEE, 2017: 63–70.Google Scholar

  • [27]

    Moreno J, Ramos-Castro J, Movellan J, Parrado E, Rodas G, Capdevila L. Facial video-based photoplethysmography to detect HRV at rest. Int J Sports Med 2015; 36: 474–480.PubMedWeb of ScienceCrossrefGoogle Scholar

  • [28]

    Poh M-Z, McDuff DJ, Picard RW. Advancements in noncontact, multiparameter physiological measurements using a webcam. IEEE Trans Biomed Eng 2011; 58: 7–11.CrossrefPubMedWeb of ScienceGoogle Scholar

  • [29]

    Qi H, Guo Z, Chen X, Shen Z, Wang ZJ. Video-based human heart rate measurement using joint blind source separation. Biomed Signal Process Control 2017; 31: 309–320.Web of ScienceCrossrefGoogle Scholar

  • [30]

    Rodríguez AM, Castro JR. Pulse rate variability analysis by video using face detection and tracking algorithms. In: 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Milano, Italy. 2015: 5696–5699.Google Scholar

  • [31]

    Ruminski J. Reliability of pulse measurements in videophotoplethysmography. Metrol Meas Syst 2016; 23.Google Scholar

  • [32]

    Sarkar A, Abbott AL, Doerzaph Z, Sykes K. Evaluation of video magnification for nonintrusive heart rate measurement. In: 2016 IEEE First International Conference on Control, Measurement and Instrumentation (CMI), Kolkata, India. 2016: 494–498.Google Scholar

  • [33]

    Scholkmann F, Boss J, Wolf M. An efficient algorithm for automatic peak detection in noisy periodic and quasi-periodic signals. Algorithms 2012; 5: 588–603.CrossrefGoogle Scholar

  • [34]

    Soleymani M, Lichtenauer J, Pun T, Pantic M. A multimodal database for affect recognition and implicit tagging. IEEE T Affect Comput 2012; 3: 42–55.CrossrefWeb of ScienceGoogle Scholar

  • [35]

    Sun Y, Hu S, Azorin-Peris V, Kalawsky R, Greenwald S. Noncontact imaging photoplethysmography to effectively access pulse rate variability. J Biomed Opt 2013; 18: 061205–061205.Web of SciencePubMedGoogle Scholar

  • [36]

    Sun Y, Thakor N. Photoplethysmography revisited: from contact to noncontact, from point to imaging. IEEE Trans Biomed Eng 2016; 63: 463–477.CrossrefWeb of SciencePubMedGoogle Scholar

  • [37]

    Malik M, Bigger JT, Camm AJ, et al. T. F. of The European Society of Cardiology, the North American Society for Pacing, and Electrophysiology, Heart rate variability: Standards of measurement, physiological interpretation, and clinical use. Ann Noninvas Electrocardiol 1996; 1: 151–181.Google Scholar

  • [38]

    Tarassenko L, Villarroel M, Guazzi A, Jorge J, Clifton DA, Pugh C. Non-contact video-based vital sign monitoring using ambient light and auto-regressive models. Institute of Physics and Engineering in Medicine 2014; 35: 807–831.Google Scholar

  • [39]

    Tarvainen MP, Ranta-Aho PO, Karjalainen PA. An advanced detrending method with application to hrv analysis. IEEE Trans Biomed Eng 2002; 49: 172–175.CrossrefPubMedGoogle Scholar

  • [40]

    van Gastel M, Stuijk S, de Haan G. Motion robust remote-PPG in infrared. IEEE Trans Biomed Eng 2015; 62: 1425–1433.CrossrefPubMedWeb of ScienceGoogle Scholar

  • [41]

    Verkruysse W, Svaasand LO, Nelson JS. Remote plethysmographic imaging using ambient light. Opt Express 2008; 16: 21434–21445.CrossrefPubMedWeb of ScienceGoogle Scholar

  • [42]

    Viola P, Jones M. Rapid object detection using a boosted cascade of simple features. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Kauai, Hawaii, USA. 2001: 511.Google Scholar

  • [43]

    Wieringa FP, Mastik F, van der Steen AF. Contactless multiple wavelength photoplethysmographic imaging: a first step toward “SpO2 camera” technology. Ann Biomed Eng 2005; 33: 1034–1041.CrossrefPubMedGoogle Scholar

  • [44]

    Wu T, Blazek V, Schmitt HJ. Photoplethysmography imaging: a new noninvasive and noncontact method for mapping of the dermal perfusion changes. In: EOS/SPIE European biomedical optics week. International Society for Optics and Photonics 2000: 62–70.Google Scholar

  • [45]

    Zaunseder S, Heinke A, Trumpp A, Malberg H. Heart beat detection and analysis from videos. In: 2014 IEEE 34th International Conference on Electronics and Nanotechnology (ELNANO). IEEE, Kyiv, Ukraine. 2014: 286–290.Google Scholar

  • [46]

    Zhang X, Hu W, Xie N, Bao H, Maybank S. A robust tracking system for low frame rate video. Int J Comput Vis 2015; 115: 279–304.CrossrefWeb of ScienceGoogle Scholar

About the article

Received: 2016-11-30

Accepted: 2017-10-09

Published Online: 2017-11-14

Published in Print: 2019-02-25


Citation Information: Biomedical Engineering / Biomedizinische Technik, Volume 64, Issue 1, Pages 53–65, ISSN (Online) 1862-278X, ISSN (Print) 0013-5585, DOI: https://doi.org/10.1515/bmt-2016-0234.

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