Heart diseases prediction based on ECG signals' classification using a genetic-fuzzy system and dynamical model of ECG signals

Abstract The early detection of abnormal heart conditions is vital to identify heart problems and avoid sudden cardiac death. The people with similar heart conditions almost have similar electrocardiogram (ECG) signals. By analyzing the ECG signals’ patterns one can predict arrhythmias. Since the conventional methods of arrhythmia detection rely on observing morphological features of the ECG signals which are tedious and very time consuming, the automatic detection of arrhythmia is more preferable. In order to automate detection of heart diseases an adequate algorithm is required which could classify the ECG signals with unknown features according to the similarities between them and the ECG signals with known features. If this classifier can find the similarities precisely, the probability of arrhythmia detection is increased and this algorithm can become a useful means in laboratories. In this article a new classification method is presented to classify ECG signals more precisely based on dynamical model of the ECG signal. In this proposed method a fuzzy classifier is constructed and its simulation results indicate that this classifier can segregate the ECGs with an accuracy of 93.34%. To further improve the performance of this classifier, genetic algorithm is applied where the accuracy in prediction is increased up to 98.67%. This proposed method increases the accuracy of the ECG classification regarding more precise arrhythmia detection.

[1]  Michael Small,et al.  Deterministic nonlinearity in ventricular fibrillation. , 2000, Chaos.

[2]  W.J. Tompkins,et al.  A patient-adaptable ECG beat classifier using a mixture of experts approach , 1997, IEEE Transactions on Biomedical Engineering.

[3]  Yorgos Goletsis,et al.  Automated ischemic beat classification using genetic algorithms and multicriteria decision analysis , 2004, IEEE Transactions on Biomedical Engineering.

[4]  Jürgen Fell,et al.  Nonlinear analysis of continuous ECG during sleep II. Dynamical measures , 2000, Biological Cybernetics.

[5]  Ying Sun,et al.  Assessment of Chaotic Parameters in Nonstationary Electrocardiograms by Use of Empirical Mode Decomposition , 2004, Annals of Biomedical Engineering.

[6]  Teck Wee Chua,et al.  Non-singleton genetic fuzzy logic system for arrhythmias classification , 2011, Eng. Appl. Artif. Intell..

[7]  Pere Caminal,et al.  Methods derived from nonlinear dynamics for analysing heart rate variability , 2009, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[8]  M. Arthanari,et al.  ECG Feature Extraction Techniques - A Survey Approach , 2010, ArXiv.

[9]  N. G. B. Amma,et al.  Cardiovascular disease prediction system using genetic algorithm and neural network , 2012, 2012 International Conference on Computing, Communication and Applications.

[10]  Pablo Laguna,et al.  A wavelet-based ECG delineator: evaluation on standard databases , 2004, IEEE Transactions on Biomedical Engineering.

[11]  Elif Derya Übeyli,et al.  Adaptive neuro-fuzzy inference system for classification of ECG signals using Lyapunov exponents , 2009, Comput. Methods Programs Biomed..

[12]  Karsten Sternickel,et al.  Automatic pattern recognition in ECG time series , 2002, Comput. Methods Programs Biomed..

[13]  T. M. Nazmy,et al.  Adaptive Neuro-Fuzzy Inference System for classification of ECG signals , 2010, 2010 The 7th International Conference on Informatics and Systems (INFOS).

[14]  Yean-Ren Hwang,et al.  ECG Feature Extraction and Classification Using Cepstrum and Neural Networks , 2008 .

[15]  Desmond C. McLernon,et al.  A COMPREHENSIVE MODEL USING MODIFIED ZEEMAN MODEL FOR GENERATING ECG SIGNALS , 2005 .

[16]  Mitsuo Gen,et al.  Genetic algorithms and engineering optimization , 1999 .

[17]  R. Saatchi,et al.  Feature extraction and classification of electrocardiogram (ECG) signals related to hypoglycaemia , 2003, Computers in Cardiology, 2003.

[18]  Patrick E. McSharry,et al.  Method for generating an artificial RR tachogram of a typical healthy human over 24-hours , 2002, Computers in Cardiology.

[19]  Patrick E. McSharry,et al.  A realistic coupled nonlinear artificial ECG, BP, and respiratory signal generator for assessing noise performance of biomedical signal processing algorithms , 2004, SPIE International Symposium on Fluctuations and Noise.

[20]  H. Nakajima,et al.  Real-time discrimination of ventricular tachyarrhythmia with Fourier-transform neural network , 1999, IEEE Transactions on Biomedical Engineering.

[21]  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.

[22]  E. C. Zeeman,et al.  Differential equations for the heartbeat and nerve impulse , 1973 .

[23]  D. T. Kaplan,et al.  Techniques for analyzing complexity in heart rate and beat-to-beat blood pressure signals , 1990, [1990] Proceedings Computers in Cardiology.

[24]  Patrick E. McSharry,et al.  A dynamical model for generating synthetic electrocardiogram signals , 2003, IEEE Transactions on Biomedical Engineering.

[25]  Yi Zhao,et al.  Evidence Consistent with Deterministic Chaos in Human Cardiac Data: Surrogate and Nonlinear Dynamical Modeling , 2008, Int. J. Bifurc. Chaos.

[26]  A. Murray,et al.  Relation between heart rate and pulse transit time during paced respiration , 2001, Physiological measurement.