Classification of Cardiac Arrhythmias with TSK Fuzzy System using Genetic Algorithm

Detection of cardiac arrhythmias, particularly ventricular fibrillation (VF), and ventricular tachycardia (VT) have been highly regarded and has done several works in this field. In this study, a method based on the Takagi-Sugeno-Kang (TSK) fuzzy system for ECG arrhythmia detection and classification of normal sinus rhythm (NSR), ventricular fibrillation (VF) and ventricular tachycardia (VT) has been used. ECG arrhythmia signals have been obtained from MIT-BIH database. At the first, preprocessing is performed on the signals to get a signal without any noise. Then two features of ECG signals include an average period T (i.e. the time interval between two R peaks) and amplitude of QRS complex, are used as inputs to fuzzy classifier. The triangular membership functions for converts crisp input values (features of ECG signals) to the fuzzy values are used to provide the fuzzy system. Using genetic algorithms, optimization rules with membership functions by minimizing the error function, and convert them to proper rules and membership functions for classifying arrhythmias do with high accuracy. Finally, we achieved the classification accuracy for normal signals (NSR) 91.66%, for VT signals 92.86% and for VT signals equal to 100%. We obtained the overall accuracy of the classifier 93.33%. Also, sensitivity for NSR signals is equal 92.30%, for VT signals is 93.33% and for VF signals is equal to 100%. Specificity for NSR, VT and VF signals is equal to 94.44%, 93.57% and 100% respectively. The simply of propose method can be considered as its major advantage.