ECG Classification Algorithm Based on Deep Ensemble Learning

Abstract

Cardiovascular disease has become a major global public health concern due to its high mortality rate. As a core non-invasive diagnostic tool, electrocardiogram (ECG) automatic classification is critical for the early screening of heart diseases. To address the problems of low efficiency in manual diagnosis, the inability of single deep learning models to simultaneously capture morphological and temporal features in ECG classification, and insufficient robustness under class-imbalanced scenarios, this paper proposes a deep ensemble learning algorithm named CBR-Stacking. The algorithm uses 1D-CNN, CNN-BiLSTM, and ResNet1D as heterogeneous base models to capture local morphological features, local-temporal fused features, and deep residual features of ECG signals, respectively. Following the Stacking ensemble strategy, the outputs of base models are taken as meta-features and fed into a logistic regression meta-classifier to obtain the final classification decision. Meanwhile, Z-score normalization and class weight method are adopted for data preprocessing to alleviate amplitude differences and class imbalance. Experimental results on the PTB myocardial infarction dataset and MIT-BIH arrhythmia dataset show that the CBR-Stacking model achieves accuracies of 99.55% and 98.86% in binary classification and five-class classification tasks, respectively, outperforming single base models and traditional machine learning methods in all evaluation metrics. Moreover, the time consumption of the model ensemble stage accounts for less than 0.1%, ensuring stable running efficiency. The proposed model effectively integrates the complementary advantages of multi-architecture deep learning models, improving the accuracy, robustness, and generalization ability of ECG classification, and provides an efficient and reliable solution for intelligent auxiliary diagnosis of electrocardiograms.