Comprehensive Analysis of Heart Rate Variability Using Various Methods
International Journal of Electrical and Electronics Engineering |
© 2024 by SSRG - IJEEE Journal |
Volume 11 Issue 2 |
Year of Publication : 2024 |
Authors : Hadeer Ahmed Mahmoud, Yehia S. Mohamed |
How to Cite?
Hadeer Ahmed Mahmoud, Yehia S. Mohamed, "Comprehensive Analysis of Heart Rate Variability Using Various Methods," SSRG International Journal of Electrical and Electronics Engineering, vol. 11, no. 2, pp. 129-143, 2024. Crossref, https://doi.org/10.14445/23488379/IJEEE-V11I2P114
Abstract:
Over the past two decades, the analysis of Heart Rate Variability (HRV) has garnered considerable traction, serving as a pivotal tool in studying various disease pathologies. HRV analyses encompass methodologies aimed at quantifying Heart Rate (HR) variations non-invasively. This study aimed to conceive, assess, and apply an accessible HRV analysis. The presented analysis integrates four primary categories of HRV techniques. The first two methods are the statistical and time-domain analysis. Moreover, the frequency-domain analysis, nonlinear analysis, and time-frequency analysis have been applied. Assessments of the presented analysis were conducted by conducting HRV analysis on simulated data. The results obtained from simulations indicated the reliability of the proposed analysis as an HRV analysis procedure. The presented analysis stands as a valuable resource, offering researchers an effective tool for conducting HRV analysis.
Keywords:
ECG signals, HRV, IBIs, Time-domain analysis, Frequency-Domain Analysis, Nonlinear analysis, Time-Frequency Domain Analysis.
References:
[1] John T. Ramshur, “Design, Evaluation, and Application of Heart Rate Variability Analysis Software (HRVAS),” Electronic Theses and Dissertations, University of Memphis, pp. 1-108, 2010.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Fatma Sayed Ibrahim et al., “Pre-Processing Steps for Genome-Wide High-Density NARAC Dataset Facilitates its Haplotype Block Partitioning,” Journal of Advanced Engineering Trends, vol. 40, no. 2, pp. 61-69, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[3] William E. Shell Md et al., “Sentra PM (A Medical Food) and Trazodone in the Management of Sleep Disorders,” Journal of Central Nervous System Disease, vol. 4, pp. 65-72, 2012.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Amira Mofreh Ibrahim, Kamel Rahouma, and Hesham Fathy Aly Hamed, “Deep Neural Network for Breast Tumor Classification through Histopathological Image,” Journal of Advanced Engineering Trends, vol. 42, no. 1, pp. 121-129, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Michael Brennan, Marimuthu Palaniswami, and Peter Kamen, “Poincaré Plot Interpretation Using a Physiological Model of HRV Based on a Network of Oscillators,” American Journal of Physiology-Heart and Circulatory Physiology, vol. 283, no. 5, pp. H1873-H1886, 2002.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Michael Gaebler et al., “Heart Rate Variability and its Neural Correlates during Emotional Face Processing in Social Anxiety Disorder,” Biological Psychology, vol. 94, no. 2, pp. 319-330, 2013.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Teresa Henriques et al., “Nonlinear Methods Most Applied to Heart-Rate Time Series: A Review,” Entropy, vol. 22, no. 3, pp. 1-40, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Antonio Cevese et al., “Baroreflex and Oscillation of Heart Period at 0.1 Hz Studied by Alpha-Blockade and Cross-Spectral Analysis in Healthy Humans,” Journal of Physiology, vol. 531, no. 1, pp. 235-244, 2001.
[CrossRef] [Google Scholar] [Publisher Link]
[9] R. Acharya et al., “Classification of Cardiac Abnormalities Using Heart Rate Signals,” Medical and Biological Engineering and Computing, vol. 42, pp. 288-293, 2004.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Haitham M. Al-Angari, and Alan V. Sahakian, “Use of Sample Entropy Approach to Study Heart Rate Variability in Obstructive Sleep Apnea Syndrome,” IEEE Transactions on Biomedical Engineering, vol. 54, no. 10, pp. 1900-1904, 2007.
[CrossRef] [Google Scholar] [Publisher Link]
[11] R.E. Nabors-Oberg et al., “The Effects of Controlled Smoking on Heart Period Variability,” IEEE Engineering in Medicine and Biology Magazine, vol. 21, no. 4, pp. 65-70, 2002.
[CrossRef] [Google Scholar] [Publisher Link]
[12] D. Bonaduce et al., “Effects of Converting Enzyme Inhibition on Heart Period Variability in Patients with Acute Myocardial Infarction,” Circulation, vol. 90, no. 1, pp. 108-113, 1994.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Yoko Tsui Caldwell, and Patrick R. Steffen, “Adding HRV Biofeedback to Psychotherapy Increases Heart Rate Variability and Improves the Treatment of Major Depressive Disorder,” International Journal of Psychophysiology, vol. 131, pp. 96-101, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Jakub S. Gąsior et al., “Normative Values for Heart Rate Variability Parameters in School-Aged Children: Simple Approach Considering Differences in Average Heart Rate,” Frontiers in Physiology, vol. 9, pp. 1-12, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Jermana Lopes de Moraes et al., “Stratification of Cardiopathies Using Photoplethysmographic Signals,” Informatics in Medicine Unlocked, vol. 20, pp. 1-8, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Reena Ranpuria et al., “Heart Rate Variability (HRV) in Kidney Failure: Measurement and Consequences of Reduced HRV,” Nephrology Dialysis Transplantation, vol. 23, no. 2, pp. 444-449, 2008.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Wilhelm Burger, and Mark J. Burge, Digital Image Processing: An Algorithmic Introduction Using Java, 2nd ed., Springer Nature, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Aparecida Maria Catai et al., “Heart Rate Variability: Are you Using It Properly? Standardisation Checklist of Procedures,” Brazilian Journal of Physical Therapy, vol. 24, no. 2, pp. 91-102, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[19] N. Lippman, K.M. Stein, and B.B. Lerman, “Comparison of Methods for Removal of Ectopy in Measurement of Heart Rate Variability,” American Journal of Physiology-Heart and Circulatory Physiology, vol. 267, no. 1, pp. H411-H418, 1994.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Luca Faes et al., “Comparison of Methods for the Assessment of Nonlinearity in Short-Term Heart Rate Variability under Different Physiopathological States,” Chaos: An Interdisciplinary Journal of Nonlinear Science, vol. 29, no. 12, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Jakob Karolus et al., “Mirror, Mirror on the Wall: Exploring Ubiquitous Artifacts for Health Tracking,” MUM ‘21: Proceedings of the 20th International Conference on Mobile and Ubiquitous Multimedia, pp. 148-157, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[22] K. Shafqat, S.K. Pal, and P.A. Kyriacou, “Evaluation of Two Detrending Techniques for Application in Heart Rate Variability,” 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Lyon, France, pp. 267-270, 2007.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Marek Malik et al., “Heart Rate Variability: Standards of Measurement, Physiological Interpretation, and Clinical Use,” European Heart Journal, vol. 17, no. 3, pp. 354-381, 1996.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Marcus Vollmer, “HRVTool - An Open-Source Matlab Toolbox for Analyzing Heart Rate Variability,” 2019 Computing in Cardiology Conference (CinC), Singapore, pp. 1-4, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Dominique Makowski et al., “NeuroKit2: A Python Toolbox for Neurophysiological Signal Processing,” Behavior Research Methods, vol. 53, pp. 1689-1696, 2021.
[CrossRef] [Google Scholar] [Publisher Link]