Evaluation of Heart Disease Diagnosis Approach using ECG Images

Among illnesses, heart diseases are accounted for as one of the most responsible for deaths. Precise and fast diagnoses increase the patient's chances to receive treatment time. A non-invasive and low-cost way to diagnose it is by using Electrocardiogram (ECG). In this paper, we propose a way to diagnosis two types of heart arrhythmia, by using the ECG record as an image. To access the performance of our system, five feature extraction methods well-known in literature are used along with five different classifiers are tested. We were able to identify heart disorders with over 96.00% of accuracy, using a vanilla neural-network, Multilayer Perceptron (MLP), and Local Binary Patterns (LBP) from ECG images. This investigation has shown promising results from a medical point-of-view.