Automated ECG Classification for Myocardial Infarction Diagnosis Using CNN and Wavelet Transform

In this study, we propose a methodology for classifying electrocardiogram (ECG) signals into normal and myocardial infarction (MI) classes. The methodology consists of three main steps: pre-processing, segmentation, and classification. ECG signals obtained from the PTB database are initially subjected to pre-processing using the Daubechies wavelet transform to filter out noise and enhance signal quality. Subsequently, the signals are segmented into 651-sample segments and further reduced to 500 samples per segment for dimensionality reduction. These segmented signals serve as input data for a convolutional neural network (CNN) model, which extracts relevant features and performs the classification task. The proposed methodology achieves an impressive classification accuracy of 97.8% for ECG signals. These findings highlight the effectiveness of our approach in accurately distinguishing between normal and MI ECG patterns.