Details

Title

Comparison of multiband filtering, empirical mode decomposition and short-time fourier transform used to extract physiological components from long-term heart rate variability

Journal title

Metrology and Measurement Systems

Yearbook

2021

Volume

vol. 28

Issue

No 4

Affiliation

Adamczyk, Krzysztof : Department of Electronic and Photonic Metrology, Wrocław University of Science and Technology, B. Prusa Str. 53/55, 50-317 Wrocław, Poland ; Polak, Adam G. : Department of Electronic and Photonic Metrology, Wrocław University of Science and Technology, B. Prusa Str. 53/55, 50-317 Wrocław, Poland

Authors

Keywords

heart rate variability ; nonstationary signal analysis ; multiband filtering ; empirical mode decomposition ; short-time Fourier transform ; Hilbert transform

Divisions of PAS

Nauki Techniczne

Coverage

643-660

Publisher

Polish Academy of Sciences Committee on Metrology and Scientific Instrumentation

Bibliography

[1] Chylinski, M., & M., Szmajda, M. (2018). Statistical methods for analysing deceleration and acceleration capacity of the heart rate. In Hunek, W., & Paszkiel, S. (Eds.). Advances in Intelligent Systems and Computing: Vol. 720. Biomedical Engineering and Neuroscience. (pp. 85–97). Springer. https://doi.org/10.1007/978-3-319-75025-5_9
[2] Siecinski, S.,Kostka, P. S.,&Tkacz, E. J. (2020). Heart rate variability analysis on electrocardiograms, seismocardiograms and gyrocardiograms on healthy volunteers. Sensors, 20(16), 4522. https://doi.org/ 10.3390/s20164522
[3] Acharya, R. U., Joseph, P. K., Kannathal, N., Choo, M. L., & Suri, J. S. (2006). Heart rate variability: A review. Medical and Biological Engineering and Computing, 44(12), 1031–1051. https://doi.org/10.1007/s11517-006-0119-0
[4] Shaffer, F., & Ginsberg, J. P. (2017). An overview of heart rate variability metrics and norms. Frontiers in Public Health, 5, 258. https://doi.org/10.3389/fpubh.2017.00258
[5] Goldoozian L. S., Zahedi, E., & Zarzoso, V. (2017). Time-varying assessment of heart rate variability parameters using respiratory information. Computers in Biology and Medicine, 89, 355–367. https://doi.org/10.1016/j.compbiomed.2017.07.022
[6] Boardman, A., Schlindwein, F. S., Rocha, A. P., & Leite, A. (2002). A study on the optimum order of autoregressive models for heart rate variability. Physiological Measurement, 23(2), 325–336. https://doi.org/10.1088/0967-3334/23/2/308
[7] Karim, N., Hasan, J. A., & Ali, S. S. (2011). Heart rate variability – A review. Australian Journal of Basic and Applied Sciences, 7(1), 71–77.
[8] Stein, P. K., & Pu, Y. (2012). Heart rate variability, sleep and sleep disorders. Sleep Medicine Reviews, 16(1), 47–66. https://doi.org/10.1016/j.smrv.2011.02.005
[9] Bernardi, L., Valle, F., Coca, M., Calciati, A., & Sleight, P. (1996). Physical activity influences heart rate variability and very-low-frequency components in Holter electrocardiograms. Cardiovascular Research, 32(2), 234–237. https://doi.org/10.1016/0008-6363(96)00081-8
[10] Aoki, K., Stephens, D. P., & Johnson, J. M. (2001). Diurnal variation in cutaneous vasodilator and vasoconstrictor systems during heat stress. American Journal of Physiology. Regulatory, Integrative and Comparative Physiology, 281(2), 591–595. https://doi.org/10.1152/ajpregu.2001.281.2.R591
[11] Fleisher, L. A., Frank, S. M., Sessler, D. I., Cheng, C., Matsukawa, T., & Vannier, C. A. (1996). Thermoregulation and heart rate variability. Clinical Science, 90(2), 97–103. https://doi.org/10.1042/cs0900097
[12] Akselrod. S., Gordon, D., Ubel. F. A., Shannon, D. C., Barger, A. C., & Cohen, R. J. (1981). Power spectrum analysis of heart rate fluctuation: A quantitative probe of beat-to-beat cardiovascular control. Science, 213(4504), 220–222. https://doi.org/10.1126/science.6166045
[13] Porter, G. A., Jr., & Rivkees, S. A. (2001). Ontogeny of humoral heart rate regulation in the embryonic mouse. American Journal of Physiology. Regulatory, Integrative and Comparative Physiology, 281(2), 401–407. https://doi.org/10.1152/ajpregu.2001.281.2.r401
[14] Stampfer, H. G., & Dimmitt, S. B. (2013). Variations in circadian heart rate in psychiatric disorders: Theoretical and practical implications. ChronoPhysiology and Therapy, 3, 41–50. https://doi.org/10.2147/CPT.S43623
[15] Jelinek, H. F., Huang, Z. Q., Khandoker, A. H., Chang, D., & Kiat, H. (2013). Cardiac rehabilitation outcomes following a 6-week program of PCI and CABG patients. Frontiers in Physiology, 4, 302. https://doi.org/10.3389/fphys.2013.00302
[16] Li, H., Kwong, S., Yang, L., Huang, D., & Xiao, D. (2011). Hilbert-Huang transform for analysis of heart rate variability in cardiac health. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 8(6), 1557–1567. https://doi.org/10.1109/TCBB.2011.43
[17] Task Force of the European Society of Cardiology and the North American Society of Pacing Electrophysiology. (1996). Heart rate variability: Standards of measurement, physiological interpretation, and clinical use. Circulation, 93(5), 1043–1065. https://doi.org/10.1161/01.CIR.93.5.1043
[18] Wen, F., & He, F.-T. (2011). An efficient method of addressing ectopic beats: new insight into data preprocessing of heart rate variability analysis. Journal of Zhejiang University Science B, 12, 976–982. https://doi.org/10.1631/jzus.b1000392
[19] Mendez, M. O., Bianchi, A. M., Matteucci, M., Cerutti, S., & Penzel, T. (2009). Sleep apnea screening by autoregressive models from a single ECG lead. IEEE Transactions on Biomedical Engineering, 56(12), 2838–2850. https://doi.org/10.1109/tbme.2009.2029563
[20] Penzel, T., Kantelhardt, J. W., Grote, L., Peter, J. H., & Bunde, A. (2003). Comparison of detrended fluctuation analysis and spectral analysis for heart rate variability in sleep and sleep apnea. IEEE Transactions on Biomedical Engineering, 50(10), 1143–1151. https://doi.org/10.1109/TBME.2003.817636
[21] Chan, H. L., Chou, W. S., Chen, S. W., Fang, S. C., Liou, C. S., & Hwang, Y. S. (2005). Continuous and online analysis of heart rate variability. Journal of Medical Engineering and Technology, 29(5), 227–234. https://doi.org/10.1080/03091900512331332587
[22] Kudrynski, K., & Strumillo, P. (2015). Real-time estimation of the spectral parameters of heart rate variability. Biocybernetics and Biomedical Engineering, 35(4), 304–316. https://doi.org/10.1016/ j.bbe.2015.05.002
[23] Echeverria, J. C., Crowe, J. A., Woolfson, M. S., & Hayes-Gill, B. R. (2001). Application of empirical mode decomposition to heart rate variability analysis. Medical and Biological Engineering and Computing, 39(4), 471–479. https://doi.org/10.1007/bf02345370
[24] Billman, G. E. (2011). Heart rate variability – A historical perspective. Frontiers in Physiology, 2, 86. https://doi.org/10.3389/fphys.2011.00086
[25] Romano, M., Faiella, G., Clemente, F., Iuppariello, L., Bifulco, P., & Cesarelli, M. (2016). Analysis of foetal heart rate variability components by means of empirical mode decomposition. IFMBE Proceedings, 57, 71–74. https://doi.org/10.1007/978-3-319-32703-7_15
[26] Montano, N., Porta, A., Cogliati, C., Costantino, G., Tobaldini, E., Casali, K. R., & Iellamo, F. (2009). Heart rate variability explored in the frequency domain: A tool to investigate the link between heart and behavior. Neuroscience and Biobehavioral Reviews, 33(2), 71–80. https://doi.org/10.1016/j.neubiorev.2008.07.006
[27] Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., Yen, N.-C., Tung, C. C., & Liu, H. H. (1998). The empirical mode decomposition and the Hilbert spectrum for nonlinear and nonstationary time series analysis. Proceedings of the Royal Society of London A, 454(1971), 903–995. https://doi.org/10.1098/rspa.1998.0193
[28] Chen, M., He, A., Feng, K., Liu, G., & Wang, Q. (2019). Empirical mode decomposition as a novel approach to study heart rate variability in congestive heart failure assessment. Entropy, 21(12), 1169. https://doi.org/10.3390/e21121169
[29] Balocchi, R., Menicucci, D., Santarcangelo, E., Sebastiani, L., Gemignani, A., Ghelarducci, B., & Varanini, M. (2004). Deriving the respiratory sinus arrhythmia from the heartbeat time series using empirical mode decomposition. Chaos, Solitons & Fractals, 20(1), 171–177. https://doi.org/10.1016/S0960-0779(03)00441-7
[30] Ortiz, M. R., Bojorges, E. R., Aguilar, S. D., Echeverria, J. C., Gonzalez-Camarena, R., Carrasco, S., Gaitan, M. J., & Martinez, A. (2005). Analysis of high frequency fetal heart rate variability using empirical mode decomposition. Computers in Cardiology, France, 675–678. https://doi.org/10.1109/ CIC.2005.1588192
[31] Helong, L., Yang, L., & Daren, H. (2008). Application of Hilbert-Huang transform to heart rate variability analysis. 2nd International Conference on Bioinformatics and Biomedical Engineering, China, 648–651. https://doi.org/10.1109/ICBBE.2008.158
[32] Neto, E. P. S., Custaud, M. A., Cejka, J. C., Abry, P., Frutoso, J., Gharib, C., & Flandrin, P. (2004). Assessment of cardiovascular autonomic control by the empirical mode decomposition. Methods of Information in Medicine, 43(1), 60–65. https://doi.org/10.1055/s-0038-1633836
[33] Ihlen, E. A. F. (2009). A comparison of two Hilbert spectral analyses of heart rate variability. Medical & Biological Engineering & Computing, 47(10), 1035–1044. https://doi.org/10.1007/ s11517-009-0500-x
[34] Eleuteri, A., Fisher, A. C., Groves, D., & Dewhurst, C. J. (2012). An efficient time-varying filter for detrending and bandwidth limiting the heart rate variability tachogram without resampling: MATLAB open-source code and internet web-based implementation. Computational and Mathematical Methods in Medicine, 2012, Article 578785. https://doi.org/10.1155/2012/578785
[35] Fisher, A. C., Eleuteri, A., Groves, D., & Dewhurst, C. J. (2012). The Ornstein–Uhlenbeck third-order Gaussian process (OUGP) applied directly to the un-resampled heart rate variability (HRV) tachogram for detrending and low-pass filtering. Medical and Biological Engineering and Computing, 50(7), 737–742. https://doi.org/10.1007/s11517-012-0928-2
[36] Varanini, M., Macerata, A., Emdin, M., & Marchesi, C. (1994). Non linear filtering for the estimation of the respiratory component in heart rate. Computers in Cardiology, USA, 565–568. https://doi.org/10.1109/CIC.1994.470129
[37] Estévez, M., Machado, C., Leisman, G., Estévez-Hernández, T., Arias-Morales, A., Machado, A., & Montes-Brown, J. (2016). Spectral analysis of heart rate variability. International Journal on Disability and Human Development, 15(1), 5–17. https://doi.org/10.1515/ijdhd-2014-0025
[38] McCraty, R., & Shaffer, F. (2015). Heart rate variability: New perspectives on physiological mechanisms, assessment of self-regulatory capacity, and health risk. Global Advances in Health and Medicine, 4(1), 46–61. https://doi.org/10.7453/gahmj.2014.073
[39] Nunan, D., Sandercock, G. R. H., & Brodie, D. A. (2010). A quantitative systematic review of normal values for short-term heart rate variability in healthy adults. Pacing and Clinical Electrophysiology, 33(11), 1407–1417. https://doi.org/10.1111/j.1540-8159.2010.02841.x
[40] Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P. C., Mark, R., Mietus, J. E., Moody, G. B., Peng, C. K.,&Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation, 101(23), 215–220. https://doi.org/10.1161/01.cir.101.23.e215
[41] Terzano, M. G., Parrino, L., Sherieri, A., Chervin, R., Chokroverty, S., Guilleminault, C., Hirshkowitz, M., Mahowald, M., Moldofsky, H., Rosa, A., Thomas, R., & Walters, A. (2001). Atlas, rules, and recording techniques for the scoring of cyclic alternating pattern (CAP) in human sleep. Sleep Medicine, 2(6), 537–553. https://doi.org/10.1016/s1389-9457(01)00149-6
[42] Jun, S., Szmajda, M., Khoma, V., Khoma, Y., Sabodashko, D., Kochan, O., & Wang, J. (2020). Comparison of methods for correcting outliers in ECG-based biometric identification. Metrology and Measurement Systems, 27(3), 387–398. https://doi.org/10.24425/mms.2020.132784
[43] Hsu, M.-K., Sheu, J.-C., & Hsue, C. (2011). Overcoming the negative frequencies: instantaneous frequency and amplitude estimation using osculating circle method. Journal of Marine Science and Technology, 19(5), 514–521. https://doi.org/10.6119/JMST.201110_19(5).0007
[44] Bayly E. J. (1968). Spectral analysis of pulse frequency modulation in the nervous systems. IEEE Transactions on Biomedical Engineering, 15(4), 257–265. https://doi.org/10.1109/TBME.1968.4502576
[45] Mateo, J., & Laguna, P. (1996). New heart rate variability time-domain signal construction from the beat occurrence time and the IPFM model. Computers in Cardiology, USA, 185–188. https://doi.org/10.1109/CIC.1996.542504
[46] de Boer, R. W., Karemaker, J. M., & Strackee, J. (1985). Spectrum of a series of point events, generated by the integral pulse frequency modulation model. Medical and Biological Engineering and Computing, 23(2), 138–142. https://doi.org/10.1007/BF02456750
[47] Nakao, M., Norimatsu, M., Mizutani, Y., & Yamamoto, M. (1997). Spectral distortion properties of the integral pulse frequency modulation model. IEEE Transactions on Biomedical Engineering, 44(5), 419–426. https://doi.org/10.1109/10.568918


Date

2021.12.22

Type

Article

Identifier

DOI: 10.24425/mms.2021.137700
×