Deep Learning-Based Automatic ECG Diagnosis for Arrhythmia Identification

One of the most common medical test used to record and evaluate heart activity is the electrocardiogram (ECG), which enables diagnosis of many common heart problems. Recently, learning techniques have been used to identify patterns in healthcare data sets and have stricken success in diverse tasks to improve clinical practice. In this paper, we present an intelligent system designed to assist medical professionals in the early diagnosis of heart arrhythmia by leveraging ECG signals. To achieve this, we employ a convolutional neural network (CNN) to classify ECG signals into five distinct cardiac rhythm categories. The experimental results demonstrate the effectiveness of the proposed approach, achieving an average classification accuracy of 0.95 and a loss of 0.2, highlighting its reliability in arrhythmia detection.