An Explainable Machine Learning Approach for Heartbeat Classification Through Signal-Based Features

Cardiovascular disease (CVD) is a general term referring to several heart or blood vessels abnormality. Heart failure (HF), directly associated to (CVD), is a significant global health problem as well as the leading cause of morbidity and mortality. The early detection of this condition is crucial for patient health. Traditional diagnostic methods for HF, such as history taking and physical examination, are often insufficient and require the use of advanced techniques such as Electrocardiogram (ECG). This study aims to extract temporal and morphological features from (ECG) signals and compare different Machine Learning (ML) classification models to enable rapid diagnosis and provide interpretable predictions. Specifically, we propose a Light Gradient Boosting (LGBM) model that can discriminate Normal Sinus Rhythm (NSR) and Arrhythmia (ARR) with a high accuracy of 0.99, achieving a Precision of 1.00, Recall of 0.99, and f1-score of 0.99 in the (NSR) class, and Precision of 0.99, Recall of 1.00, and f1-score of 0.99 in the (ARR) class, respectively. In addition, eXplainable Artificial Intelligence (XAI) analysis is performed to explain the model predictions.