Basab Bijoy Purkayastha
ABSTRACT
The degree of complexity associated with the dynamics of the human heart is different for different categories of cardiac abnormalities. A quantitative measure of the complexities associated with cardiac dynamics can be obtained with a nonlinear study like a Multifractal analysis of the electrocardiogram signals. Here in this work, we have modeled the heart as a nonlinear dynamical system and obtained its phase space structure or embedded attractor reconstructed out of the ECG time series corresponding to four channels. Then, studied the multifractal behavior of the embedded attractor in order to quantify the degree of complexities associated with the dynamics of the heart. In particular, we have derived a few parameters out of the multifractal singularity spectrum and used them as a discriminative measure between various kinds of cardiac conditions. We have demonstrated a gradient boosting-based parametric classification model to discriminate between different kinds of arrhythmias and normal sinus rhythms. This work achieves considerably high accuracy in classifying different variants of cardiac disorders when trained with the parameters obtained from the multifractal singularity spectrum associated with the embedded attractor reconstructed out of the ECG time series.
- D. J. Christini, K. M. Stein, S. M. Markowitz, S. Mittal, D. J. Slotwiner and B. B. Lerman. 1999. The role of nonlinear dynamics in cardiac arrhythmia control. In Heart Dis.1(4), pp. 190-200.Google Scholar
- Chen, C., Jin, Y., Lo, I. L., Zhao, H., Sun, B., Zhao, Q., Zheng, J., and Zhang, X. D. 2017. Complexity Change in Cardiovascular Disease. In International Journal of biological sciences, Vol. 13(10), pp. 1320–1328. https://doi.org/10.7150/ijbs.19462Google Scholar
- Wang, J., Ning, X. and Chen, Y. 2003. Multifractal analysis of electrocardiogram taken from healthy and unhealthy adult subjects. In Physica A: Statistical Mechanics and its Applications, 323, 561–568Google Scholar
- Trine Krogh-Madsen and David J. Christini. 2012. Nonlinear Dynamics in Cardiology. In Annual Review of Biomedical Engineering, 14, 179-203. https://doi.org/10.1146/annurev-bioeng-071811-150106Google ScholarCross Ref
- Nayak SK, Bit A, Dey A, Mohapatra B, Pal K. 2018. A Review on the Nonlinear Dynamical System Analysis of Electrocardiogram Signal. In Journal of Healthcare Engineering, 2018:6920420. doi: 10.1155/2018/6920420Google ScholarCross Ref
- Karma, A. 2013. Physics of cardiac arrhythmogenesis. In Annu. Rev. Condens. Matter Phys. 4, 313–337Google ScholarCross Ref
- Kantelhardt, J.W. 2012. Fractal and Multifractal Time Series. In Meyers, R. (eds) Mathematics of Complexity and Dynamical Systems. Springer, New York, NY. 463–487. https://doi.org/10.1007/978-1-4614-1806-130Google Scholar
- Webster, J. G. 2009. Medical Instrumentation: Application And Design, 3rd edn,. In John Wiley & SonsGoogle Scholar
- Helen Mary Mercy Cleetus, Dilbag Singh. 2014. Multifractal application on electrocardiogram. In Journal of Medical Engineering & Technology, 38:1, 55-61 https://doi.org/10.3109/03091902.2013.849298Google ScholarCross Ref
- Easwaramoorthy, D., and Uthayakumar, R. 2010. Analysis of biomedical EEG signals using Wavelet Transforms and Multifractal Analysis. In IEEE International Conference for Communication Control and Computing Technologies, Ramanathapuram, India, 544–549Google Scholar
- Diosdado, A.M. 2009. Analysis of the Relation between Complexity and Multifractality in Cardiac Interbeat Intervals Time Series. In D¨ossel, O., Schlegel, W.C. (eds) World Congress on Medical Physics and Biomedical Engineering, Munich, Germany, IFMBE Proceedings, Springer, Berlin, Heidelberg, 25/4. https://doi.org/10.1007/978-3-642-03882-%202%20399Google Scholar
- Kurths, J. 1995. Quantitative analysis of heart rate variability. In Chaos: An Interdisciplinary Journal of Nonlinear Science 5, 88–94.Google ScholarCross Ref
- Ivanov, P. C. 1999. Multifractality in human heartbeat dynamics. In Nature, 399, 461–465.Google ScholarCross Ref
- Rodriguez E, Lerma C, Echeverria JC, Alvarez-Ramirez J. 2008. ECG scaling properties of cardiac arrhythmias using detrended fluctuation analysis. In Physiol Meas. 29(11), 1255-1266. doi: 10.1088/0967-3334/29/11/002.Google ScholarCross Ref
- R. Krishnam, S. Chatlapalli, H. Nazeran, E. Haltiwanger and Y. Pamula. 2005. Detrended Fluctuation Analysis: A Suitable Long-term Measure of HRV Signals in Children with Sleep Disordered Breathing. In IEEE Engineering in Medicine and Biology 27th Annual Conference, 1174-1177, doi: 10.1109/IEMBS.2005.1616632.Google Scholar
- Amaral, L. A. N. 2001. Behavioral-independent features of complex heartbeat dynamics. In Physical Review Letters 86, 6026.Google ScholarCross Ref
- Ana Gavrovska, Goran Zaji´c, Irini Reljin,and Branimir Reljin. 2013. Classification of Prolapsed Mitral Valve versus Healthy Heart from Phonocardiograms by Multifractal Analysis. In Hindawi Publishing Corporation, 2013 (376152). doi:10.1155/2013/376152Google ScholarCross Ref
- Daoming Zhang and Cong Wang and Chuangye Li and Weizhong Dai. 2019. Multi-fractal detrended fluctuation half-spectrum analysis of HRV. In The Journal of Engineering, 2019, 22, 8315 – 8318, DOI: 10.1049/joe.2019.1067.Google ScholarCross Ref
- A. M. Aguilar Molina, R. I. Rojas Jim´enez, and A. Mu˜noz Diosdado. 2019. Multifractal analysis of ECG time series of stress tests in healthy subjects. In AIP Conference Proceedings 2090, 050001. https://doi.org/10.1063/1.5095916Google Scholar
- Dezhao Jiao, Zikuan Wang, Jin Li, Feilong Feng, Fengzhen Hou. 2020. The chaotic characteristics detection based on multifractal detrended fluctuation analysis of the elderly 12-lead ECG signals. In Physica A: Statistical Mechanics and its Applications, 540,123234. doi:10.1016/j.physa.2019.123234.Google Scholar
- Timothy D. Sauer. Attractor reconstruction. In Scholarpedia, 1(10):1727. doi:10.4249/scholarpedia.1727.Google Scholar
- Takens, F. 1981. Dynamical systems and turbulence,” in Lecture notes in mathematics 898, 366.Google Scholar
- Harikrishnan, K. P., Misra, R., Ambika, G. and Amritkar, R. E. 2009. Computing the multifractal spectrum from time series: an algorithmic approach. In Chaos: An Interdisciplinary Journal of Nonlinear Science 19, 043129.Google ScholarCross Ref
- Shekatkar, S.M., Kotriwar, Y., Harikrishnan, K.P. 2017. Detecting abnormality in heart dynamics from multifractal analysis of ECG signals. In Sci Rep 7, 15127. https://doi.org/10.1038/s41598-017-15498-zGoogle ScholarCross Ref
- Zheng, J., Zhang, J., Danioko, S. 2020. A 12-lead electrocardiogram database for arrhythmia research covering more than 10,000 patients. In Sci Data 7, 48 (2020). https://doi.org/10.1038/s41597-020-0386-xGoogle ScholarCross Ref
- Zheng, J. 2019. A 12- lead electrocardiogram database for arrhythmia research covering more than 10,000 patients. In figshare, https://doi.org/10.6084/m9.figshare.c.4560497.v2 (2019).Google Scholar
Index Terms
- Cardiac Anomaly Detection using Embedded Attractors Reconstructed from Multichannel ECG
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