Abstract
Remote photoplethysmography (rPPG) has been at the forefront recently, thanks to its capacity in estimating non-contact physiological parameters such as heart rate and heart rate variability (Wang et al. in FBB 6:33, 2018). rPPG signals are typically extracted from facial videos by performing spatial averaging to obtain temporal RGB traces. Although this spatial averaging simplifies computation, it is accompanied by loss of essential spatial information which might reveal interesting relationships between signals from different spatial regions. In this article, we present a novel algorithm adapted from generalized eigenvalue decomposition (GEVD) to estimate this spatial rPPG distribution. GEVD is an extremely versatile algorithm that finds uses in signal and image processing and analytical problems such as principal component analysis and Fisher discriminant analysis (Ghojogh et al. in Tutorial 2: 1–8, 2019)(Han and Clemmensen in PR 49:43-54, 2016). It is performed using the QZ algorithm (Moler and Stewart in JNA 10(2):241–256, 2010), which in turn uses Householder transformations (Householder in JACM 5(4):339–342, 1958) to extract generalized eigenvectors of a pair of matrices. We adapt the QZ algorithm for the domain of spatio-temporal biomedical signals such as remote photoplethysmography (rPPG), electrocardiography and electroencephalography signals. We call this algorithm Temporal-QZ, which employs vectorization techniques to extract generalized eigenvectors over spatial data points simultaneously. We validate this extension in the domain of remote photoplethysmography (rPPG) measurement, for the estimation of spatial rPPG distribution of skin.









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Code and links to videos available at https://gitlab.com/rich4rd.macwan/temporal-qz.
References
Aarts, L.A.M., Jeanne, V., Cleary, J.P., Lieber, C., Nelson, J.S., Bambang Oetomo, S., Verkruysse, W.: Non-contact heart rate monitoring utilizing camera photoplethysmography in the neonatal intensive care unit: a pilot study. Early Hum. Dev. 89(12), 943–948 (2013). https://doi.org/10.1016/j.earlhumdev.2013.09.016. NIHMS150003
Bobbia, S., Macwan, R., Benezeth, Y., Mansouri, A., Dubois, J.: Unsupervised skin tissue segmentation for remote photoplethysmography. Pattern Recognit. Lett. (2017). https://doi.org/10.1016/j.patrec.2017.10.017
ClinicalTrialsgov-[Internet]: Can remote photoplethysmography be used for contactless vital sign acquisition in a healthcare setting? a prospective comparative study. identifier: Nct04489407 (2020). https://clinicaltrials.gov/ct2/show/study/NCT04489407
De Haan, G., Jeanne, V.: Robust pulse rate from chrominance-based rPPG. IEEE Trans. Biomed. Eng. 60(10), 2878–2886 (2013). https://doi.org/10.1109/TBME.2013.2266196
de Haan, G., van Leest, A.: Improved motion robustness of remote-PPG by using the blood volume pulse signature. Physiol. Meas. 35(9), 1913–1926 (2014). https://doi.org/10.1088/0967-3334/35/9/1913
Douglas, R., Elliott, J.: Vectorization, Part 2: Why and What? https://www.quantifisolutions.com/vectorization-part-2-why-and-what (2017). https://www.quantifisolutions.com/vectorization-part-2-why-and-what
Francis, J.G.F.: The QR transformation, a unitary analogue to LR transformation: part I. Comput. J. 4(3), 265–271 (1961)
Ghojogh, B., Karray, F., Crowley, M.: Eigenvalue and Generalized Eigenvalue Problems: Tutorial. arXiv (2), 1–8 (2019). arXiv:1903.11240
Han, X., Clemmensen, L.: Regularized generalized eigen-decomposition with applications to sparse supervised feature extraction and sparse discriminant analysis. Pattern Recognit 49, 43–54 (2016). https://doi.org/10.1016/j.patcog.2015.07.008
Hertzman, A.B.: Photoelectric plethysmography of the fingers and toes in man. Exp. Biol. Med. 37(3), 529–534 (1937). https://doi.org/10.3181/00379727-37-9630
Householder, A.S.: Unitary triangularization of a nonsymmetric matrix. J. ACM 5(4), 339–342 (1958). https://doi.org/10.1145/320941.320947
Huang, P.W., Wu, B.J., Wu, B.F.: A Heart Rate Monitoring Framework for Real-World Drivers Using Remote Photoplethysmography. IEEE J Biomed Health Inform PP (2020)
Kamshilin, A.A., Nippolainen, E., Sidorov, I.S., Vasilev, P.V., Erofeev, N.P., Podolian, N.P., Romashko, R.V.: A new look at the essence of the imaging photoplethysmography. Sci. Rep. 5, 10494 (2015). https://doi.org/10.1038/srep10494
Kazemi, V., Sullivan, J.: One millisecond face alignment with an ensemble of regression trees. CVPR (2014). https://doi.org/10.13140/2.1.1212.2243
Kolda, T.G., Bader, B.W.: Tensor decompositions and applications. SIAM Rev. 51(3), 455–500 (2009). https://doi.org/10.1137/07070111X. arXiv:1404.3905
Kranjec, J., Beguš, S., Geršak, G., Drnovšek, J.: Non-contact heart rate and heart rate variability measurements: a review. Biomed. Signal Process Control 13(1), 102–112 (2014). https://doi.org/10.1016/j.bspc.2014.03.004
Kumar, M., Veeraraghavan, Ashok, Sabharwal, Ashutosh: DistancePPG: robust non-contact vital signs monitoring using a camera. Biomed. Opt. Express 6(5), 1565 (2015). https://doi.org/10.1364/BOE.6.001565. arXiv:1502.08040
Latifis, I., Parashar, K., Dimitroulakos, G., Cappelle, H., Lezos, C., Masselos, K., Catthoor, F.: A MATLAB vectorizing compiler targeting application-specific instruction set processors. ACM Trans. Des. Autom. Electron. Syst. 22(2), 1–28 (2017). https://doi.org/10.1145/2996182
Li, X., Chen, J., Zhao, G., Pietikainen, M., Pietik, M., Pietikäinen, M.: Remote heart rate measurement from face videos under realistic situations. In: Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit, pp. 4264–4271 (2014). https://doi.org/10.1109/CVPR.2014.543, http://openaccess.thecvf.com/content_cvpr_2014/papers/Li_Remote_Heart_Rate_2014_CVPR_paper.pdf
Liang, X., Humos, A.A., Pei, T.: Vectorization and Parallelization of Loops in C / C++. In: Proc Int Conf Front Educ Comput Sci Comput Eng, pp. 203–206 (2018)
Lu, H., Plataniotis, K.N., Venetsanopoulos, A.N.: Mpca: Multilinear principal component analysis of tensor objects. IEEE Trans. Neural Netw. 19(1), 18–39 (2008). https://doi.org/10.1109/TNN.2007.901277
Lucas, B., Kanade, T.: An iterative image registration technique with an application to stereo vision (ijcai). vol. 81 (1981)
Macwan, R., Benezeth, Y., Mansouri, A.: Heart rate estimation using Remote Photoplethysmography with Multi-objective Optimization. Biomed Signal Process Control (2018a)
Macwan, R., Benezeth, Y., Nakamura, K., Gomez, R., Wu, Y., Mansouri, A.: Parameter-free adaptive step-size multiobjective optimization applied to remote photoplethysmography. In: Biomed. Heal. Informatics Conf., vol. 2018-Janua, pp. 267–270 (2018c). https://doi.org/10.1109/BHI.2018.8333420
Macwan, R., Bobbia, S., Benezeth, Y., Dubois, J., Mansouri, A.: Periodic Variance Maximization using Generalized Eigenvalue Decomposition applied to Remote Photoplethysmography estimation. In: IEEE Conf. Comput. Vis. Pattern Recognit. Work., pp. 1445–1453 (2018d). https://doi.org/10.1109/CVPRW.2018.00181, https://hal-univ-bourgogne.archives-ouvertes.fr/hal-01830541
Macwan, R., Benezeth, Y., Mansouri, A.: Remote photoplethysmography with constrained ICA using periodicity and chrominance constraints. Biomed. Eng. Online 17(1), 22 (2018b). https://doi.org/10.1186/s12938-018-0450-3
MATLAB and Statistics Toolbox Release 2016b, The MathWorks, Inc, Natick, Massachusetts US (2021) Vectorization. https://fr.mathworks.com/help/matlab/matlab_prog/vectorization.html
Mcduff, D.J., Estepp, J.R., Piasecki, A.M., Blackford, E.B.: A Survey of Remote Optical Photoplethysmographic Imaging Methods. In: 37th Annu. Int. Conf. IEEE EMBC, pp. 6398–6404 (2015)
McDuff, D., Gontarek, S., Picard, R.: Improvements in remote cardiopulmonary measurement using a five band digital camera. IEEE Trans. Biomed. Eng. 61(10), 2593–2601 (2014). https://doi.org/10.1109/TBME.2014.2323695
Moler, C.B., Stewart, G.W.: An algorithm for generalized matrix eigenvalue problems. Soc. Ind. Appl. Math. J. Numer. Anal. 10(2), 241–256 (2010)
Nowara, E.M., Marks, T.K., Mansour, H., Veeraraghavan, A.: SparsePPG: Towards Driver Monitoring Using Camera-Based Vital Signs Estimation in Near-Infrared. In: IEEE Conf. CVPR (2018). http://openaccess.thecvf.com/CVPR2018_workshops/content_CVPR_2018/papers/w27/Nowara_SparsePPG_Towards_Driver_CVPR_2018_paper.pdf
PD, W., CY, L.: Python for Data Analysis, vol 344. O’Reilly, Beijing (2012). https://doi.org/10.1097/MAJ.0b013e318228aef8. http://www.embase.com/search/results?subaction=viewrecord&from=export&id=L51559769%0A, arXiv:1011.1669v3
Peters, G., Wilkinson, J.H.: Eigenvectors of real and complex matrices by LR and QR triangularizations. Numer. Math. 16(3), 181–204 (1970). https://doi.org/10.1007/BF02219772
Poh, M.Z., McDuff, D.J., Picard, R.W.: Non-contact, automated cardiac pulse measurements using video imaging and blind source separation. Opt. Express 18(10), 10762–10774 (2010). https://doi.org/10.1364/OE.18.010762
Poh, M.Z., McDuff, D.J., Picard, R.W.: Advancements in noncontact, multiparameter physiological measurements using a webcam. IEEE Trans. Biomed. Eng. 58(1), 7–11 (2011). https://doi.org/10.1109/TBME.2010.2086456
Qu, F., Wang, S., Yan, W., Li, H., Wu, S., Fu, X.: Cas(me)\(^2\) : a database for spontaneous macro-expression and micro-expression spotting and recognition. IEEE Trans. Affect. Comput. 9(4), 424–436 (2018). https://doi.org/10.1109/TAFFC.2017.2654440
Sameni, R., Jutten, C., Shamsollahi, M., Electrocardiogram, M.: Multichannel electrocardiogram decomposition using periodic component analysis. IEEE Trans. Biomed. Eng. 1935–1940 (2008)
Srivastav, M.: Vectorization: Writing C/C++ code in VECTOR Format (2012). https://software.intel.com/en-us/articles/vectorization-writing-cc-code-in-vector-format, https://software.intel.com/en-us/articles/vectorization-writing-cc-code-in-vector-format
Tulyakov, S., Alameda-Pineda, X., Ricci, E., Yin, L., Cohn, J.F., Sebe, N., Sommarive, V., Kessler, F.B., Sommarive, V.: Self-Adaptive Matrix Completion for Heart Rate Estimation from Face Videos under Realistic Conditions. In: 2016 IEEE Conf Comput Vis Pattern Recognit, pp. 2396–2404 (2016). https://doi.org/10.1109/CVPR.2016.263, http://ieeexplore.ieee.org/document/7780632/
Tursa, J.: MTIMESX: Fast Matrix Multiply with Multi-Dimensional Support (2011). https://mathworks.com/matlabcentral/fileexchange/25977-mtimesx-fast-matrix-multiply-with-multi-dimensional-support
von Arx, T., Tamura, K., Yukiya, O., Lozanoff, S.: The face: a vascular perspective—a literature review. Swiss Dent. J. 128, 382–392 (2018)
Wang, W., Stuijk, S., De Haan, G.: Exploiting spatial redundancy of image sensor for motion robust rPPG. IEEE Trans. Biomed. Eng. 62(2), 415–425 (2015). https://doi.org/10.1109/TBME.2014.2356291. arXiv:15334406
Wang, W., Den Brinker, A., Stuijk, S., De Haan, G.: Algorithmic principles of remote-PPG. IEEE Trans. Biomed. Eng. 64(7), 1479–1491 (2016a). https://doi.org/10.1109/TBME.2016.2609282
Wang, W., Stuijk, S., De Haan, G.: A novel algorithm for remote photoplethysmography: spatial subspace rotation. IEEE Trans. Biomed. Eng. 63(9), 1974–1984 (2016b). https://doi.org/10.1109/TBME.2015.2508602
Wang, W., den Brinker, A.C., Stuijk, S., de Haan, G.: Amplitude-selective filtering for remote-PPG. Biomed. Opt. Express 8(3), 1965–1980 (2017). https://doi.org/10.1364/BOE.8.001965
Wang, C., Pun, T., Chanel, G.: A comparative survey of methods for remote heart rate detection from frontal face videos. Front. Bioeng. Biotechnol. 6, 33 (2018)
Wilkinson, J.H.: The Algebraic Eigenvalue Problem. Oxford University Press, Oxford (1965)
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Macwan, R., Benezeth, Y. & Mansouri, A. Generalized Eigenvalue Decomposition Applied to Estimation of Spatial rPPG Distribution of Skin. J Math Imaging Vis 63, 807–820 (2021). https://doi.org/10.1007/s10851-021-01025-3
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DOI: https://doi.org/10.1007/s10851-021-01025-3