Abstract

ST segment is the most important diagnostic parameter for finding coronary heart disease (CHD). Based on ST segment which has been extracted from electrocardiogram (ECG) data with wavelet transform, we investigated the classification of five different shapes of ST segment using fuzzy adaptive resonance theory mapping (ARTMAP) neural networks. The proposed method was demonstrated by the data from the standard MIT/BIH ECG database. The results show that fuzzy ARTMAP could be used to distinguish the shapes of ST segment successfully.

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