Multi-class Heart Disease Classification Using Multi-lead ECG Features and Ensemble Learning

Cardiovascular diseases (CVDs) are the leading causes of global mortality and require an early and precise diagnosis. This work presents an automated multiclass classifier for diagnosing cardiac disease from electrocardiogram (ECG) images through image processing and machine learning techniques. The proposed framework consists of three steps, including preprocessing, feature extraction, and ensemble learning. Initially, the ECG image undergoes a comprehensive preprocessing pipeline that includes lead segmentation, grayscale conversion, Gaussian filtering, and Otsu thresholding. The contour-based features are extracted and then reduced by PCA to preserve discriminative information. Finally, multiple machine learning models, including K-nearest neighbors (KNN), logistic regression, and support vector machines (SVM), are ensembled using voting and stacking classifiers to improve the performance of the proposed framework. The proposed ensemble model is evaluated on a public dataset that consists of ECG images that are categorized into four classes: normal, abnormal, myocardial infarction (MI), and history of MI. The proposed ensemble model attained the highest classification accuracy of 98.06% and outperforms the existing pre-trained and state-of-the-art models.