Arrhythmia Detection on ECG Signals by Using Empirical Mode Decomposition

One of the main causes of sudden deaths is heart disease. Early detection and treatment of cardiac arrhythmias prevent the problem from reaching sudden deaths. The purpose of this study is to develop an arrhythmia detection algorithm based on Empirical Mode Decomposition (EMD). This algorithm consists of four steps: Preprocessing, Empirical Mode Decomposition, feature extraction and classification. Six arrhythmia types were used for differentiate normal and arrhythmic signals obtained from the MIT-BIH Arrhythmia database. These are normal (N), left bundle branch block (LBBB), right bundle branch block (RBBB), premature ventricular contraction (PVC), paced beat and atrial premature beats (APB). Three different classifiers were used to classify ECG signals. The method achieves better result with accuracy of 87% using linear discriminant analysis (LDA) classifier for detection of normal and arrhythmic signals.

[1]  A. Murray,et al.  Recognition of ventricular fibrillation using neural networks , 1994, Medical and Biological Engineering and Computing.

[2]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[3]  Lu Qing,et al.  Adaptive Noise Removal of ECG Signal Based On Ensemble Empirical Mode Decomposition , 2011 .

[4]  W. J. Tompkins,et al.  Detecting ventricular fibrillation , 1995 .

[5]  Peter W. Macfarlane,et al.  Artificial neural networks for the diagnosis of atrial fibrillation , 1993 .

[6]  K. V. Bisina,et al.  Optimized estimation of power spectral density , 2017, 2017 International Conference on Intelligent Computing and Control Systems (ICICCS).

[7]  Monica Subashini,et al.  Analysis of Electrocardiograph (ECG) Signal for the Detection of Abnormalities Using MATLAB , 2014 .

[8]  A. Al-Fahoum,et al.  Combined wavelet transformation and radial basis neural networks for classifying life-threatening cardiac arrhythmias , 1999, Medical & Biological Engineering & Computing.

[9]  Yang Wang,et al.  A short-time multifractal approach for arrhythmia detection based on fuzzy neural network , 2001, IEEE Transactions on Biomedical Engineering.

[10]  Jaya Prakash Sahoo,et al.  Analysis of ECG signal for Detection of Cardiac Arrhythmias , 2011 .

[11]  P. Sleight,et al.  Physical activity influences heart rate variability and very-low-frequency components in Holter electrocardiograms. , 1996, Cardiovascular research.

[12]  N.V. Thakor,et al.  Multiway sequential hypothesis testing for tachyarrhythmia discrimination , 1994, IEEE Transactions on Biomedical Engineering.

[13]  Dimitrios Hatzinakos,et al.  Ecg in biometric recognition: time dependency and application challenges , 2011 .

[14]  J. Jenkins,et al.  A comparison of four new time-domain techniques for discriminating monomorphic ventricular tachycardia from sinus rhythm using ventricular waveform morphology , 1991, IEEE Transactions on Biomedical Engineering.

[15]  L. Khadra,et al.  Detection of life-threatening cardiac arrhythmias using the wavelet transformation , 1997, Medical and Biological Engineering and Computing.

[16]  Philip de Chazal,et al.  A Patient-Adapting Heartbeat Classifier Using ECG Morphology and Heartbeat Interval Features , 2006, IEEE Transactions on Biomedical Engineering.

[17]  C. Kamath.  ECG beat classification using features extracted from teager energy functions in time and frequency domains , 2011 .

[18]  N. Thakor,et al.  Ventricular tachycardia and fibrillation detection by a sequential hypothesis testing algorithm , 1990, IEEE Transactions on Biomedical Engineering.

[19]  Behboud Mashoufi,et al.  A new personalized ECG signal classification algorithm using Block-based Neural Network and Particle Swarm Optimization , 2016, Biomed. Signal Process. Control..

[20]  Yasser M. Kadah,et al.  Study of features based on nonlinear dynamical modeling in ECG arrhythmia detection and classification , 2002, IEEE Transactions on Biomedical Engineering.

[21]  Kang-Ming Chang,et al.  Arrhythmia ECG Noise Reduction by Ensemble Empirical Mode Decomposition , 2010, Sensors.

[22]  Naomie Salim,et al.  Arrhythmia recognition and classification using combined linear and nonlinear features of ECG signals , 2016, Comput. Methods Programs Biomed..

[23]  F. Bereksi-Reguig,et al.  Detection of QRS Complexes in ECG Signals Based on Empirical Mode Decomposition , 2011 .