Wasit University discusses master's thesis entitled Medical Diagnosis based on Analysis of Electrocardiogram Signals (Ecg) using computer vision and classification techniques

The Faculty of Engineering at Wasit University discussed the tagged master's thesis (medical diagnosis based on the analysis of ecg signals using computer vision and classification techniques).

The letter, prepared by student Nour Khudhair Abbas, aims to diagnose heart disease by identifying 17 types of heart disease (arrhythmias).

The proposed algorithm includes the construction of a 2D-CNN neural network trained using ECG signal images collected from the MIT-BIH public database, the letter said. This algorithm is best suited for classifying image data and thereby reducing preprocessing 2D_CNN have been trained and validated.

The results of the tests addressed in the letter showed that the proposed method achieved a rating accuracy of 96.67% and a 0.004% error. In addition to the superior accuracy achieved by this method, it was compared to previous methods and found that this work has less processing time and complexity, as well as by dealing with images in this way that can be integrated with other applications or mobile devices.