Effect of the Input Window Size in Arrhythmia classification with Multilayer Perceptron Network Structures

In this work, the arrhythmias in the electrocardiograph (ECG) signals are analyzed by using multi-layer perceptron (MLP) network. For training MLP network back-propagation with adaptive learning rate method is used. Feature vectors obtained from consecutive sample values of each peak in different window sizes are normalized and used for training the networks. Performances of different classifiers are examined depending on the average value of sensitivity, specificity, selectivity and accuracy of the classifiers. The results show that for the proposed classifier the optimal feature vector is a 71-point vector with 35 before and 35 after the R peak point of the ECG.