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In this work, we present an ECG delineation and the automated diagnosis of coronary artery disease in the electrocardiogram (ECG). In preprocessing stage, the baseline wander (BLW) and 60 Hz power line interference (PLI) were removed using discrete wavelet transform (DWT). The QRS detection is carried out using Daubechies (Db4) DWT. Feature extraction and classification is done using a convolutional neural network (CNN) containing three convolutional layers, three max-pooling layers, and three fully connected layers. The standard 12 lead ECG signals of 50 healthy subjects and 50 myocardial infarction subjects (MI) of one minute are obtained from the PhysikalischTechnische Bundesanstalt (PTB) database. We achieved an accuracy of 94.83%. sensitivity of 94.75%, and specificity of 94.93% on PTB database.

A Wavelet-Based ECG Delineation and Automated Diagnosis of Myocardial Infarction in PTB Database