Application of adaptive neuro-fuzzy inference system for detection of electrocardiographic changes in patients with partial epilepsy using feature extraction

In this study, a new approach based on adaptive neuro-fuzzy inference system (ANFIS) was presented for detection of electrocardiographic changes in patients with partial epilepsy. Decision making was performed in two stages: feature extraction using the wavelet transform (WT) and the ANFIS trained with the backpropagation gradient descent method in combination with the least squares method. Two types of electrocardiogram (ECG) beats (normal and partial epilepsy) were obtained from the MIT-BIH database. The proposed ANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. Some conclusions concerning the impacts of features on the detection of electrocardiographic changes were obtained through analysis of the ANFIS. The performance of the ANFIS classifier was evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed ANFIS classifier has potential in detecting the electrocardiographic changes in patients with partial epilepsy. q 2004 Elsevier Ltd. All rights reserved.