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Wavelet-Based Classification of Myocardial Ischemia, Arrhythmia, Congestive Heart Failure and Sleep Apnea
Abstract:This paper presents wavelet based classification of various heart diseases. Electrocardiogram signals of different heart patients have been studied. Statistical natures of electrocardiogram signals for different heart diseases have been compared with the statistical nature of electrocardiograms for normal persons. Under this study four different heart diseases have been considered as follows: Myocardial Ischemia (MI), Congestive Heart Failure (CHF), Arrhythmia and Sleep Apnea. Statistical nature of electrocardiograms for each case has been considered in terms of kurtosis values of two types of wavelet coefficients: approximate and detail. Nine wavelet decomposition levels have been considered in each case. Kurtosis corresponding to both approximate and detail coefficients has been considered for decomposition level one to decomposition level nine. Based on significant difference, few decomposition levels have been chosen and then used for classification.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.3669275Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 240
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 S Chattopadhyay, S Chattopadhyay and A Das, “Electrocardiogram Signal Analysis for Diagnosis of Apnea”, AMSE JOURNALS-2016-Series: Modelling C; Vol. 77; N° 1; pp 28-40, July 15, 2016, ISSN: 1259-5977.
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 S. Chattopadhyay, G. Sarkar, and A. Das, “Sleep Apnea Diagnosis by DWT based Kurtosis, Radar and Histogram Analysis of Electrocardiogram”, accepted in IETE Journal of Research (Taylor & Francis).
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 S. Chattopadhyay, G. Sarkar, and A. Das, “Electrocardiogram Signal Analysis for Diagnosis of Congestive Heart Failure”, Modelling and Simulation in Science, Technology and Engineering Mathematics, Proceedings of MS-17, ISBN: 978-3-319-74807-8, Series: Computer Science, ISSN: 2194-5357, Vol: Advances in Intelligent System and Computing (764), Springer, Place: Kolkata, Paper-ID-MS-17-133, Date: November-4-5, 2017.
 S. Chattopadhyay, G. Sarkar, and A. Das, “Study of Arrhythmia using Wavelet Transformation based Statistical Parameter Computing of Electrocardiogram Signal”, Modelling and Simulation in Science, Technology and Engineering Mathematics, Proceedings of MS-17, ISBN: 978-3-319-74807-8, Series: Computer Science, ISSN: 2194-5357, Vol: Advances in Intelligent System and Computing (764), Springer, Place: Kolkata, Paper-ID-MS-17-139, Date: November-4-5, 2017.
 S. Chattopadhyay, G. Sarkar, and A. Das, “Spider and Histogram Assessment of Electrocardiogram for Apnea Diagnosis”, Proceedings of 10th International Conference of IMBIC on Mathematical Sciences for Advancement of Science and Technology, MSAST-2016, Vol.5 (2016), pp 149-153, December 21-23, 2016, ISBN: 978-81-925832-4-2.
 www.physionet.org, Accessed on October 20, 2018.