Key result
ConvXGB outperforms state-of-the-art models for ECG heartbeat classification with ~99% accuracy.
Why the study?
Current machine learning approaches for ECG signal classification rely on hand-crafted feature extraction methods or very complicated deep learning networks.
Does the ConvXGB model improve ECG heartbeat classification compared to CNN or XGBoost alone and other state-of-the-art models?
Does the ConvXGB model improve ECG heartbeat classification compared to CNN or XGBoost alone and other state-of-the-art models?
A novel ConvXGB model combining CNN and XGBoost achieved high accuracy (0.9938) in classifying ECG signals, outperforming existing models.
Supports adoption of ConvXGB for ECG diagnostics; extends hybrid ML methods in cardiac research.
ELECTROCARDIOGRAM (ECG) signals are reliable in identifying and monitoring patients with various cardiac diseases and severe cardiovascular syndromes, including arrhythmia and myocardial infarction (MI). Thus, cardiologists use ECG signals in diagnosing cardiac diseases. Machine learning (ML) has also proven its usefulness in the medical field and in signal classification. However, current ML approaches rely on hand-crafted feature extraction methods or very complicated deep learning networks. This paper presents a novel method for feature extraction from ECG signals and ECG classification using a convolutional neural network (CNN) with eXtreme Gradient Boosting (XBoost), ConvXGB. This model was established by stacking two convolutional layers for automatic feature extraction from ECG signals, followed by XGBoost as the last layer, which is used for classification. This technique simplified ECG classification in comparison to other methods by minimizing the number of required parameters and eliminating the need for weight readjustment throughout the backpropagation phase. Furthermore, experiments on two famous ECG datasets–the Massachusetts Institute of Technology–Beth Israel Hospital (MIT-BIH) and Physikalisch-Technische Bundesanstalt (PTB) datasets–demonstrated that this technique handled the ECG signal classification issue better than either CNN or XGBoost alone. In addition, a comparison showed that this model outperformed state-of-the-art models, with scores of 0.9938, 0.9839, 0.9836, 0.9837, and 0.9911 for accuracy, precision, recall, F1-score, and specificity, respectively.
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Rawi et al. (2022) studied Arrhythmia and myocardial infarction. ConvXGB model (CNN with XGBoost) vs. CNN alone, XGBoost alone, and state-of-the-art models was evaluated on Accuracy, precision, recall, F1-score, and specificity. The ConvXGB model outperformed state-of-the-art models for ECG heartbeat classification, achieving an accuracy of 0.9938, precision of 0.9839, and F1-score of 0.9837.
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