Intelligent system for Classification ECG Based on Convolutional Neural Networks (CNN)

The electrocardiogram (ECG) is a non-invasive procedure used to diagnose cardiovascular disorders. The 2D ECG system gives important information on the electrophysiology of heart illnesses and possible brain changes, as well as important information about the cardiovascular system’s functional characteristics. The classification proposed system detected four different classes from the ECG cases: myocardial infarction (MI), Arrhythmia, History MI, and Normal case. The suggested system used three steps for classification ECG, include: Image processing, feature extraction, and classification. The first step involves utilizing Image preprocessing methods to prepare and enhance the input images, which includes two steps: converting RGB images to a binary image and applying filters to reduce noise from a binary image. Utilizing local binary pattern algorithm, features are retrieved from the images in the second step (LBP). The collected characteristics are then supplied into a 1D "convolutional neural network" (1D-CNN) model for object detection and classification in the third step. The findings demonstrate that the suggested model of the CNN has a high accuracy rate, with a classification accuracy rate of 97 percent. The suggested method works extremely well in the categorization of the 2D ECG, as demonstrated by the low rate of false alarms and high rate of accuracy.