Enhanced ECG Signal Classification

Abstract

The increasing amount of medical data emphasizes the urgent need for efficient methods in classifying electrocardiogram (ECG) signals. While current approaches are valuable, they struggle to achieve both high sensitivity and specificity, limiting their effectiveness in timely cardiac diagnosis. These challenges underscore the importance of more robust methodologies to improve the accuracy of ECG signal classification. To tackle these issues, this research suggests a comprehensive approach using machine learning techniques. Our framework incorporates various algorithms such as Support Vector Machines (SVM), XGBoost, K-Nearest Neighbors (KNN), Logistic Regression, and an ensemble classifier. This ensemble method aims to leverage the strengths of individual models, enhancing the overall classification performance. The application of this approach shows promising results, with increased sensitivity and specificity in categorizing ECG signals. The versatility of our proposed framework has significant potential for various applications, contributing to advancements in cardiovascular health monitoring and diagnosis.