ECG Signal Classification Using AI

Abstract

ECG analysis plays a vital role in diagnosing a wide range of heart conditions. The electrocardiogram (ECG) provides detailed information about the electrical activity of the heart. A single cardiac cycle in an ECG consists of the P, QRS, and T wave segments. Analyzing and classifying ECG signals helps in understanding various cardiac abnormalities and diseases. In recent years, several techniques and research approaches have been developed for ECG signal analysis, focusing on extracting important features such as amplitude and time intervals to enable accurate classification. This paper reviews some of the previously proposed methods for arrhythmia detection and parameter extraction from ECG signals, which support data acquisition and classification processes. Furthermore, the study presents an overview of the proposed approach that utilizes Artificial Neural Networks (ANN) along with data mining techniques through intelligent data mining tools. This combination enhances the efficiency and accuracy of ECG signal classification and supports improved diagnosis of heart-related disorders.