Among various heart diseases in the world, cardiac arrhythmia is one of the deadliest diseases, affecting millions of people. Several automated approaches are exploited for the detection and classification of arrhythmia using ECG signals. Machine learning is a promising approach used intensively in recent years for the arrhythmia classification. Accordingly, in this paper, a technique is proposed for the arrhythmia classification with the introduction of a novel classifier. From the ECG signals, wave components and statistical features are extracted and the constructed feature matrix is subjected to the classification. The arrhythmia classification is performed by the proposed Cat Swarm Optimization-based Support Vector Neural network (CS-SVNN), which is the modification of the SVNN with the optimized training. Thus, the SVNN classifier with the optimally tuned weights and biases classifies the person as either arrhythmia patient or normal. The performance of the proposed technique is determined by three measures, namely accuracy, sensitivity, and specificity, and is compared with the performance attained using the existing methods. The maximum accuracy attained by the proposed CS-SVNN is 98.4%, and this proves the effectiveness of the technique in classifying the arrhythmia patients.
Pandey, S.K. and Janghel, R.R., "ECG Arrhythmia Classification Using Artificial Neural Networks", in Proceedings of 2nd International Conference on Communication, Computing and Networking, Springer, Singapore, pp. 645-652, 2019.
Mendis, B.N.P.P.S., "Global Atlas on Cardiovascular Disease Prevention and Control", World Health Organization, Geneva, 2011.
Abdalla, F.Y., Wu, L., Ullah, H., Ren, G., Noor, A. and Zhao, Y., "ECG arrhythmia classification using artificial intelligence and nonlinear and nonstationary decomposition", Signal, Image and Video Processing, pp.1-9, 2019.
Abdou, A.D., Ngom, N.F. and Niang, O., "Electrocardiograms patterns analysis using Artificial Neural Network and non-linear regression", 2018.
de Albuquerque, V.H.C., Nunes, T.M., Pereira, D.R., Luz, E.J.D.S., Menotti, D., Papa, J.P. and Tavares, J.M.R., "Robust automated cardiac arrhythmia detection in ECG beat signals", Neural Computing and Applications, vol. 29, no. 3, pp. 679-693, 2018.
Mesin L, "Heartbeat monitoring from adaptively down-sampled electrocardiogram", Computers in Biology and Medicine, vol. 84, pp. 217-225, May 2017.
Sahoo, S., Kanungo, B., Behera, S. and Sabut, S., "Multiresolution wavelet transform based feature extraction and ECG classification to detect cardiac abnormalities", Measurement, vol. 108, pp. 55-66, 2017.
Gupta, V. and Mittal, M., "KNN and PCA classifier with autoregressive modeling during different ECG signal interpretation", in proceedings of Computer Science, vol. 125, pp. 18-24, 2018.
Gutiérrez-Gnecchi, J.A., Morfin-Magaña, R., Lorias-Espinoza, D., del Carmen Tellez-Anguiano, A., ReyesArchundia, E., Méndez-Patiño, A. and Castañeda-Miranda, R., "DSP based arrythmia classification using wavelet transform and probabilistic neural network", Biomedical Signal Processing and Control, vol. 32, pp. 44- 56, 2017.
Bhagyalakshmi, V, R.V. Pujeri, Geetha D.Devanagavi, "GB-SVNN: Genetic BAT assisted support vector neural network for arrhythmia classification using ECG signals", Journal of King Saud University - Computer and Information Sciences, March 2018.
Chen, S., Hua, W., Li, Z., Li, J., Gao, X., "Heartbeat classification using projected and dynamic features of ECG signal", Biomedical Signal Processing and Control, vol. 31, pp. 165–173, January 2017.
Banerjee, S., Mitra, M., "Application of cross wavelet transform for ECG pattern analysis and classification", IEEE Transactions on Instrumentation and Measurement, Vol. 63, no. 2, pp. 326–333, February 2014.
K. Padmavathi, “Classification of ECG signal during Atrial Fibrillation using Autoregressive modelling”, Procedia Computer Science, vol. 46, pp. 53-59, 2015.
Oswaldo Ludwig, Urbano Nunes, Rui Araujo, "Eigen value decay: A new method for neural network regularization", Neurocomputing, vol. 124, pp. 33–42, 2014.
Saurabh Pal, Madhuchhanda Mitra, " Detection of ECG characteristic points using Multiresolution Wavelet Analysis based Selective Coefficient Method", Measurement, vol.43, no.2, pp.255-261, February 2010.
Chu, S.C., Tsai, P.W. and Pan, J.S., "Cat swarm optimization", In Pacific Rim international conference on artificial intelligence, Springer, Berlin, Heidelberg, pp. 854-858, August 2006.
Moody GB, Mark RG, "The impact of the MIT-BIH Arrhythmia Database", IEEE Engineering in Medicine and Biology Magazine, vol.20, no.3, pp.45-50, May-June 2001.
Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh, Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE, "PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals ", Circulation, vol.101, no.23, pp.215-220, June 13, 2000.
Narendra Kohli, Nishchal K. Verma, Abhishek Roy, "SVM based methods for arrhythmia classification in ECG ", International Conference on Computer and Communication Technology (ICCCT), pp.486 - 490, 2010.
Malay Mitra and R.K.Samanta, "Cardiac Arrhythmia Classification Using Neural Networks with Selected Features", Procedia Technology, vol.10, pp.76-84, 2013.
Mohammad Reza Homaeinezhad, Ehsan Tavakkoli1, Ali Ghaffari1, "Discrete Wavelet-based Fuzzy Network Architecture for ECG Rhythm-Type Recognition: Feature Extraction and Clustering Oriented Tuning of Fuzzy Inference System", International Journal of Signal Processing, Image Processing and Pattern Recognition, vol.4, no.3, September 2011.
Vijayakumar Polepally, K Shahu Chatrapati,"DEGSA-VMM: Dragonfly-based exponential gravitational search algorithm to VMM strategy for load balancing in cloud computing";Kybernetes, vol.67, no.6;pp.1138-1157;2018.
D. Menaga and Dr.S. Revathi,"Privacy Preserving using Bio Inspired Algorithms for Data Sanitization",International Conference on Electrical, Electronics, Computers, Communication, Mechanical and Computing (EECCMC); pp. 201-206, 2018.
RV Ravi, K Subramaniam, "Image Compression and Encryption using Optimized Wavelet Filter Bank and Chaotic Algorithm”,International Journal of Applied Engineering Research, vol. 12, no. (21); pp. 10595–10610, 2017.
S Chander, P Vijaya, P Dhyani,"Multi kernel and dynamic fractional lion optimization algorithm for data clustering", Alexandria engineering journal, vol. 57, no (1), pp.267-276, 2016.
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