Open Access Journal

ISSN : 2394 - 6849 (Online)

International Journal of Engineering Research in Electronics and Communication Engineering(IJERECE)

Monthly Journal for Electronics and Communication Engineering

Open Access Journal

International Journal of Engineering Research in Electronics and Communication Engineering(IJERECE)

Monthly Journal for Electronics and Communication Engineering

ISSN : 2394-6849 (Online)

Arrhythmia Analysis and Classification using Gaussian Mixture Model

Author : Divya K 1 Veena Karjigi 2

Date of Publication :7th May 2016

Abstract: Automatic detection and classification of ECG heart beats is of high value in diagnosis and treatment of patients with lifethreatening cardiac arrhythmia. Arrhythmia is an abnormal or irregular heart rhythm which reflects the bad condition of heart. Abnormal heartbeat comprises Right Bundle Branch Block (RBBB), Left Bundle Branch Block (LBBB), Atrial Premature Contraction (APC), and Ventricular Premature Contraction (VPC). ECG signal analysis includes QRS complex detection, P and T peak detection, feature selection and beat classification. In this paper, time interval features of ECG signal are extracted and Gaussian Mixture Model (GMM) is used to classify the ECG signals. The ECG signals used for the evaluation are taken from MIT-BIH arrhythmia database. The Experimental results obtained after classification are 84.99%, 96.6%, 93.08%, 94.48% and 62.83% for NORM, LBBB, RBBB, VPC and APC, respectively

Reference :

  1. [1] Yun-Chi Yeh, Wen-June Wang, Che Wun chiou, “Cardiac arrhythmia diagnosis method using linear discriminant analysis on ECG signals ”. Measurement Vol.42 ,pp. 778–789,2009.

    [2] Aiguang Li, Shaofeng Wang and Huabin Zheng, “A Novel Abnormal ECG Beats Detection Method”, IEEE transactions on Biomedical Engineering, Vol.1, No.4, pp. 47-51, 2010.

    [3] B. S. Raghavendra, Deep Bera and Ajit S. Bopardikar, “Cardiac Arrhythmia Detection using Dynamic Time Warping of ECG Beats in E-Healthcare Systems”

    [4] Mohamed Lamine Talbi, Abdelfateh Charef and Philip Ravier, “Arrhythmias Classification Using the Fractal Behavior of the Power Spectrum Density of the QRS Complex and ANN”, IEEE transactions on Biomedical Engineering, pp. 399-404, 2010.

    [5] Diptangshu Pandit, Li Zhang and Chengyu Liu, “An Efficient Abnormal Beat Detection Scheme from ECG Signals using Neural Network and Ensemble Classifiers,” IEEE transactions on Biomedical Engineering, pp. 399-404, 2010.

    [6] Vinod Pathangay and Satish Prasad Rath, “Arrhythmia Detection in single-lead ECG by combing Beat and Rhythmlevel Information”, IEEE transactions on Biomedical Engineering, pp. 3236-3239, 2014.

    [7] A. L. Goldberger, L. A. N. Amaral, L. Glass, J. M. Hausdorff, Ivanov PCh, R. G. Mark, J. E. Mietus, G. B. Moody, C-K. Peng, H. E. Stanley, “PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals”, Circulation 101(23):e215-e220,2003.


Recent Article