Chaotic Feature Extraction and Neuro-Fuzzy Classifier for Ecg Signal Characterization

In this paper, a neuro-fuzzy network is employed to classify the ECG beats based on the extracted chaotic features. Six groups of ECG beats (MIT-BIH Normal Sinus rhythm, BIDMC congestive heart failure, CU ventricular tachyarrhythmia, MIT-BIH atrial fibrillation, MIT-BIH Malignant Ventricular Arrhythmia and MIT-BIH supraventricular arrhythmia) are characterized by the six chaotic parameters including the largest Lyapunov exponent and average of the Lyapunov spectrum (related to the chaoticity of the signal), time lag and embedding dimension (related to the phase space reconstruction) and correlation dimension and approximate entropy of the signal (related to the complexity of the signal). Finally, six structures of the neuro-fuzzy network (in terms of the type of fuzzy set, the number of fuzzy sets per variable and the number of learning epochs) were employed to perform the ECG beats classification based on all extracted features for two lengths of the signals. It was found that all respective chaotic features are discriminative and they improve the classification rate of ECG beats. Also, it is shown that a minimum length of the signal is needed for exhibitive feature extraction and for the higher lengths of the signal (in time) no significant improvement is achieved in feature extraction and calculations. The criteria for the classification task are considered as accuracy, specificity and sensitivity which all together comprehensively demonstrate the capability and performance of the classification. Some conclusions are drawn and they are discussed at the end of the paper.