A Machine Learning Framework for the Classification of ECG Signals

The escalating volume of medical data underscores the critical need for efficient methods in classifying electrocardiogram (ECG) signals. Current approaches, while valuable, face limitations in achieving both high sensitivity and specificity, hampering their effectiveness in timely cardiac diagnosis. These challenges highlight the urgency for more robust methodologies to enhance the accuracy of ECG signal classification. To address these issues, this study proposes a comprehensive approach utilizing machine learning techniques. Our framework integrates diverse algorithms including Support Vector Machines (SVM), XGBoost, K-Nearest Neighbors (KNN), Logistic Regression and an ensemble classifier. This ensemble approach aims to capitalize on the strengths of individual models, optimizing the overall classification performance. The application of this approach demonstrates promising outcomes, with improved sensitivity and specificity in ECG signal categorization. The versatility of the proposed framework holds significant potential for widespread applications, contributing to advancements in cardiovascular health monitoring and diagnosis.