ECG Beat Classification using the Integration of S-transform, PCA and Artificial Neural Network

Electrocardiography is an important tool in diagnosing the condition of the heart. In this paper, we propose a scheme to integrate the Stockwell Transform (ST), Principal Component Analysis (PCA) and Neural Networks (NN) for ECG beat classification. The ST is employed to extract the morphological features. In addition, PCA is among considerable techniques for data reduction. A Back Propagation Neural Network (BPNN) is employed as classifier. ECG samples attributing to six different beat types are sampled from the MIT-BIH arrhythmias database for experiments. In this paper comparative study of performance of six structures such as FCM-NN, PCA-NN, FCM-ICA-NN, FCM-PCA-NN, ST-NN and ST-PCA-NN are investigated. The test results suggest that ST-PCA-NN structure can perform better and faster than other techniques.