Comparative Analysis of ECG Classification Using Neuro-Fuzzy Algorithm and Multimodal Decision Learning Algorithm: ECG Classification Algorithm

Classification of ECGs is an important task for proper identification of the signal which helps in suitable diagnosis of the patient. This paper proposes a new algorithm for ECG basic classification as normal or abnormal. As there are many existing methods for classification like support vector machine, neural networks, neuro-fuzzy algorithms and so on, the main objective of this work is to compare performance analysis of two selected methods, one with adaptive neuro-fuzzy algorithm as the existing method and the other with the proposed method i.e., multimodal decision learning algorithm. The comparative analysis deals with the parameters like true positive (TP), true negative (TN), False positive (FP), False negative (FN), False rejection ratio (FRR), false acceptance ratio (FAR), global acceptance ratio (GAR), confusion matrix (CM), Kappa coefficient (KC), Sensitivity, Specificity and Accuracy. Pre-recorded ECG signals of MIT-BIH database are used for processing, filtering, classification and performance evaluation. Simulation results indicate the ECG signal as normal or abnormal with respect to the above defined parameters.