Ischemia classification via ECG using MLP neural networks

Abstract

This paper proposes a two stage system based in neural network models to classify ischemia via ECG analysis. Two systems based on artificial neural network (ANN) models have been developed in order to discriminate inferolateral and anteroposterior ischemia from normal electrocardiogram (ECG) and other heart diseases. This method includes pre-processing and classification modules. ECG segmentation and wavelet transform were used as pre-processing stage to improve classical multilayer perceptron (MLP) network. A new set of about 800 ECG were collected from different clinics in order to create a new ECG Database to train ANN models. The best specificity of all models in the test phases was found as 88.49%, and the best sensitivity was obtained as 80.75%.

Keywords

• Classification
• ECG
• ischemia
• MLP
• DWT