ECG Signal Analysis for Abnormality Detection in Heart Beat

Vedprakash Gujiri, Vidyalankar Institute of technology; Prof. Anuradha Joshi ,Vidyalankar Institute of technology; Prof. Arun Chavan ,Vidyalankar Institute of technology

ANFIS, Arrhythmias, ECG , MIT-BIH, Subtractive clustering

Automatic recognition of cardiac arrhythmias is important for diagnosis of cardiac abnormalities. Detection of various abnormalities in the heart to identify various heart diseases can be made through an Adaptive Neuro-Fuzzy Inference System (ANFIS) preprocessed by subtractive clustering. Some recordings of the MIT-BIH database has been used for training and testing our neural network based classifier. Six types of heartbeats are classified: normal sinus rhythm, premature ventricular contraction (PVC), atrial premature contraction (APC), left bundle branch block (LBBB), right bundle branch block (RBBB), and paced beats. Our goal is to detect important characteristics of an ECG signal to determine if the heartbeat is normal or irregular. Therefore, in this paper, an expert system for Electrocardiogram (ECG) classification is analyzed. DWT is used in preprocessing for filtering ECG recordings, and extraction of some features performs the classification task.
    [1] N. Maglaveras, T. Stamkapoulos, K. Diamantaras, C. Pappas, M. Strintzis, “ECG pattern recognition and classification using non-linear transformations and neural networks: A review”, Int. J. Med. Inform. 52 (1998) 191–208. [2] S. Osowski, T.H. Linh, “ECG beat recognition using fuzzy hybrid neural network”, IEEE Trans. Biomed. Eng. 48 (2001) 1265–1271. [3] Chikh A., M., Ammar, M., Marouf, & Radja, M. (2010, April 5). A Neuro-Fuzzy Identification of ECG Beats. Tlemcen, Algeria. [4] Minas, J. S. Martins, J. H. Correia, High-Selectivity Optical Detection in Microfluidic Systems for Clinical Diagnostics, Journal of Sensors and Materials, pp.77-89, Japan, 2002. [5] R. Acharya, P. S. Bhat, S. S. Iyengar, A. Roo and S. Dua, (2002) “Classification of heart rate data using artificial neural network and fuzzy equivalence relation”, The Journal of the Pattern Recognition Society, vol. 130, pp. 101–108. [6] S. Osowski, T.H. Linh, (2001) “ECG beat recognition using fuzzy hybrid neural network”, IEEE Trans. Biomed. Eng., Vol. 48, pp. 1265-1271. [7] L. Shyu, W. Hu, (2008) “Intelligent Hybrid Methods for ECG Classification-A Review,” Journal of Medical and Biological Eng., Vol. 28, pp.1-10. [8] (2006) Digital signal processing toolbox user’s guide for use with MATLAB7. [9] GB, M., RG, M., Database, T. i.-B., & 11446209), 4.-5. (.-J. (n.d.). Retrieved July 2014, from - http://www.physionet.org/physiobank/database/mitdb/ [10] Pan, J., & Tompkins J, W. (1985). A Real-Time QRS Detection Algorithm. IEEE Transactions of Biomedical Engineering, VOL. BME-32, NO. 3. [11] Editors: Witold Pedrycz, A. (2012). ECG Signal Processing, Classification and Interpretation: A Comprehensive Framework of Computational Intelligence. New York City: Springer-Verlag London Limited2012.
Paper ID: GRDJEV01I100051
Published in: Volume : 1, Issue : 10
Publication Date: 2016-10-01
Page(s): 66 - 71