Fuzzy Inference System for Automated ECG Signal Classification: A Robust Approach to Cardiac Diagnostics

Abstract

The objective of this paper is to develop an efficient diagnostic tool for Electrocardiography (ECG) signals using a Fuzzy Inference System, referred to as FIS ECG-CD (Fuzzy Inference System for ECG Cardiac Diagnostics). The system aims to handle uncertainties inherent in medical data by applying fuzzy logic to classify ECG patterns as normal or abnormal. Key features, such as the P wave, QRS complex, and T wave, are analyzed using membership functions and expert-defined rules. This approach enhances diagnostic accuracy while maintaining simplicity and interpretability. The proposed FIS-ECG-CD system has the potential to serve as a reliable decision support tool for medical professionals, reducing diagnostic errors and improving patient outcomes.