Key result
The proposed cost-based dual convolutional attention transfer learning model achieved an F1-score of 98.08% and accuracy of 98.14% for ECG heartbeat classification, outperforming conventional machine learning models.
Why the study?
Human reading of ECGs faces challenges of inaccurate and unreliable interpretations, while ECG data present challenges due to their imbalanced nature.
Absolute Event Rate: 98.08% vs 89.45%
A novel cost-based dual convolutional attention transfer learning model achieved high accuracy (98.45%) for automated ECG heartbeat classification, addressing data imbalance issues.
The heart is a very crucial organ of the body. Concerted efforts are constantly put forward to provide adequate monitoring of the heart. A heart disorder is reported to cause a lot of hidden ailments resulting in numerous deaths. Early heart monitoring using an electrocardiogram (ECG) through the advancement of computer-aided diagnostic (CAD) systems is widely used. Meanwhile, the use of human reading of ECG results are faced with many challenges of inaccurate and unreliable interpretations. Over two decades, studies provided artificial intelligence (AI) technique using machine learning (ML) algorithms as a fast and reliable technique for ECG heartbeat classification. Moreover, in recent times, deep learning (DL) techniques have been focused on providing automatic feature extraction and better classification performance. On the other hand, the challenge with the ECG data is its imbalance nature. Therefore, this paper proposes a cost-based dual convolutional attention transfer DL model for ECG classification. The proposed model uses PhysionNet-MIT-BIH and Physikalisch-Technische Bundesanstalt (PTB) Diagnostics datasets. The first part uses the MIT-BIH for ECG categorization, while representations learned from the first classifier are used for PTB analysis through transfer learning (TL). The proposed model is evaluated and compared with well-performing conventional ML models based on their F1-score and accuracy scores. Our experimental finding show that the proposed model outperformed the well-performing ML models as well as competitive with past studies for both the classification and TL part, having obtained 98.45% for both F1-score and accuracy. The proposed model is applicable to real-life trials and experiments for ECG heartbeat and other similar domains.
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Chew et al. (2023) studied Arrhythmia and Myocardial Infarction (ECG Heartbeat Classification) (n=124,088). Cost-Based Dual ConvNet-Attention Transfer Learning Model (CB-DuConvNetA) vs. Conventional machine learning models (e.g., Random Forest, XGBoost, LightGBM) was evaluated on F1-score and accuracy for ECG heartbeat classification. The proposed cost-based dual convolutional attention transfer learning model achieved an F1-score of 98.08% and accuracy of 98.14% for ECG heartbeat classification, outperforming conventional machine learning models.
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