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Malignant Ventricular Ectopy Classification using Wavelet Transformation and Probabilistic Neural Network Classifier


  • Amity University Uttar Pradesh, India
  • Dr. RML Avadh University, Faizabad, India


Objective: The objective of this paper is to make a distinction between malignant ventricular Ectopic ElectrocarDiogram (ECG) signals from normal ones. Methods: The dataset is taken from MIT-BIH Physio bank ATM. The feature extraction has been done using the Discrete Wavelet Transformation (DWT) method. The experimental ECG signals have been decomposed till 5th level of resolution using daubechies wavelet of order 4 followed by computing various values. Based on the values, classification is performed using Probabilistic Neural Network (PNN) concept. Findings: This paper gives an independent approach for classifying malignant ventricular ectopy (MVE) ECG signals helping health care professionals. Application: The proposed method has been analyzed to be very effective in the classification of MVE ECG signals.


Discrete Wavelet Transformation, Electrocardiograph, Malignant Ventricular Ectopic Beats, MIT-BIH Database, Probabilistic Neural Network.

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  • Bigger JT Jr. Definition of benign versus malignant ventricular arrhythmias: targets for treatment. American Journal of Cardiology (AJC). 1983 Sep; 52(6):47-54.
  • Tchou PJ, Kadri N, Anderson J, Caceres JA, Jazayeri M, Akhtar M. Automatic implantable cardioverter defibrillators and survival of patients with left ventricular dysfunction and malignant ventricular arrhythmias. Annals of Internal Medicine. 1988 Oct; 109(7):529-34.
  • Andre GNG. Treating patients with ventricular ectopic beats. Education in Heart. 2006; 92(11):1707-12.
  • Swan H, Piippo K, Viitasalo M, Heikkila P, Paavonen T, Kainulainen K. A Arrhythmic disorder mapped to chromosome causes malignant polymorphic ventricular tachycardia in structurally normal hearts. Journal of the American College of Cardiology. 1999; 34(7):2035-42.
  • Higgins SL, Hummel JD, Niazi IK, Giudici MC, Worley SJ, Saxon LA. Cardiac resynchronization therapy for the treatment of heart failure in patients with intraventricular conduction delay and malignant ventricular Tachyarrhythmia’s. Journal of the American College of Cardiology (JACC). 2003 Oct; 42(8):1454-59.
  • Saraswat S, Srivastava G, Shukla SN. Review: Comparison of QRS detection algorithms. IEEE International Conference on Computing, Communication and Automation (ICCCA). Noida, 2015 May, p. 354-59.
  • Bigger TJR. Identification of patients at high risk for sudden cardiac death. The American Journal of Cardiology: ELSEVIER. 1984; 54(9):3-8.
  • Wei JY, Bulkley BH, Schaeffer AH, Greene L, Reid PR. Mitral-Valve prolapse syndrome and recurrent ventricular tachyarrhythmias: A Malignant variant refractory to conventional drug therapy. Annals of Internal Medicine. 1978 Jul; 89(1):6-9.
  • Rajoub BA. An efficient coding algorithm for the compression of ECG signals using the wavelet transform. IEEE Transactions on Biomedical Engineering. 2002 Apr; 49(4):352-62.
  • Addison PS. Wavelet transforms and the ECG: a review. Physiological Measurement. 2005; 26(5):1-46.
  • Riju BP, Sreevidya S, Smitha K. Compression and comparison of ECG signals using DWT and DWPT. Indian Journal of science and Technology. 2015; 20(4):1-5.
  • Donald F. Specht. Probabilistic Neural Networks. Neural Networks: Elsevier. 1990; 3(1):109-18.
  • Zakhich A, Desilva CJS, Attikiouzel Y. A modified Probabilistic Neural Network (PNN) for nonlinear time series analysis. IEEE International Conference on Neural Networks. Australia, 1991 Nov, p.1530-35.
  • Daneshwar MA, Noh NM. Application of radial basis function neural networks in modelling of nonlinear systems with dead band. Indian Journal of Science and Technology. 2013; 6(11):1-5.
  • Zigel Y, Cohen A, Katz A. The weighted diagnostic distortion (WDD) measure for ECG signals compression. IEEE Transactions on Bio Medical Engineering. 2000 Nov; 47(11): 1422-30.
  • Fira CMFL, Goras G. An ECG signal compression method and its validation using NNs. IEEE Transactions on BME. 2008 Apr; 55(4):1319-26.
  • Rodriguez J, Goni A, Illarramendi A. Real time classification of ECGs on a PDA. IEEE Transactions on Information Technology in Biomedicine. 2005 Mar; 9(1):23-24.
  • Ye C, Coimbra MT, Kumar BVKV. Arrhythmic detection and classification using morphological and dynamic features of ECG signals. Annual International Conference of the IEEE Engineering in Medicine and Biology. Buenos Aires, USA, 2010, p. 1918-21.
  • Patrick K, Nazih J, Jerry A, Jose A, Mohammad J, Masood A. Automatic implantable cardioverter defibrillators and survival of patients with left ventricular dysfunction and malignant ventricular arrhythmias. Annals of Internal Medicine. 1998 Oct; 109(7):529-34.


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