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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 85
 Ali Jezzini; Mohammad Ayache; Lina Elkhansa; Zein al abidin Ibrahim, ECG classification for sleep apnea detection, 2015 International Conference on Advances in Biomedical Engineering (ICABME), Year: 2015, Pages: 301 -304, DOI: 10.1109/ICABME. 2015.7323312.
 Ahnaf Rashik Hassan; Mohammad Aynal Haque, Computer-aided sleep apnea diagnosis from single-lead electrocardiogram using Dual Tree Complex Wavelet Transform and spectral features, International Conference on Electrical & Electronic Engineering (ICEEE), Year: 2015, Pages: 49 - 52, DOI: 10.1109/CEEE.2015.7428289.
 S. Patidar, R. B. Pachori, and U. R. Acharya, “Automated diagnosis of coronary artery disease using tunable-Q wavelet transform applied on heart rate signals,” Knowledge based systems, vol.82, pp.1-10, 2015.
 S. Patidar, R. B. Pachori, A. Upadhayay, and R. Acharya, “An integrated alcoholic index using Tunable-Q wavelet transform based feature extracted from EEG signals for diagnosis of alcoholism,” Applied Soft Computing, Vol. 50, Pp. 71-78, January 2017.
 Daniel Alvarez; Roberto Hornero; J. Victor Marcos; Felix del Campo, Multivariate Analysis of Blood Oxygen Saturation Recordings in Obstructive Sleep Apnea Diagnosis, IEEE Transactions on Biomedical Engineering, Year: 2010, Vol-57, Issue: 12, pp 2816-2824, DOI: 10.1109/TBME.2010.2056924.
 Alfredo Burgos; Alfredo Goni; Arantza Illarramendi; Jesús Bermudez, Real-Time Detection of Apneas on a PDA, IEEE Transactions on Information Technology in Biomedicine,2010, Vol: 14(4), pp: 995 -1002, DOI: 10.1109/TITB .2009.2034975
 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.
 S. Chattopadhyay, G. Sarkar, and A. Das, “Effect of Congestive Heart Failure in Statistical Nature of Electrocardiogram Signal”, Lectures on Modelling and Simulation, No–2, pp 163-172, ISSN: 1961-5086, AMSE, 2017.
 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).
 S. Chattopadhyay, R.R. Majhi, S. Chattopadhyay, and A. Ghosh, “Analysis of electro-cardiogram by radar and DWT based Kurtosis comparison,” Michael Faraday IET International Summit 2015, (Available in IET Digital Library and IEEE Xplore), Kolkata, India, pp. 108 (5), DOI: 10.1049/cp.2015.1704, ISBN: 978-1-78561-186-5, Sept. 12-13, 2015.
 S. Chattopadhyay, R.R. Majhi, S. Chattopadhyay, and A. Ghosh, “Radar assessment of wavelet decomposition based Skewness of ECG signals,” Proceedings of Conf. of theMichael Faraday IET International Summit 2015, (Available in IET Digital Library and IEEE Xplore) , Kolkata, India, pp. 109 (5), DOI: 10.1049/cp.2015.1705, ISBN: 978-1-78561-186-5, Sept. 12-13, 2015.
 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.