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Deep convolutional neural networks based ECG beats classification to diagnose cardiovascular conditions
Md Rashed-Al-Mahfuz1, Mohammad Ali Moni2,3, Pietro Lio'4
1Department of Computer Science and Engineering, University of Rajshahi, Rajshahi, 6205 Bangladesh.
Insights
This study introduces a novel Convolution Neural Network (CNN) model for accurate electrocardiogram (ECG) beat classification. The model achieves high accuracy, aiding in automated cardiovascular diagnosis and clinical decision-making.
Area of Science:
- Cardiology
- Biomedical Engineering
- Artificial Intelligence
Background:
- Accurate diagnosis of cardiovascular conditions relies on understanding electrocardiogram (ECG) beat features.
- Computational methods can enhance the identification of significant frequency components in temporal ECG waveforms.
Purpose of the Study:
- To propose a novel ECG beat classifier using a customized VGG16 Convolution Neural Network (CNN).
- To identify interpretable ECG frequency contributions using SHapley Additive exPlanations (SHAP) values.
- To evaluate the model's performance on benchmark ECG datasets.
Main Methods:
- Developed a customized VGG16-based CNN model processing time-frequency representations of ECG signals.
- Employed SHapley Additive exPlanations (SHAP) to interpret the contribution of ECG frequencies.
- Validated the model on the MIT-BIH arrhythmia dataset, AHA database, and LUDB.
Main Results:
- Achieved 100% classification accuracy for 2-4 classes and 99.90% for 5 classes on the MIT-BIH dataset.
- Obtained 99.91% accuracy for 5-class classification using AHA and LUDB datasets.
- SHAP values enhanced the interpretability of ECG frequency features.
Conclusions:
- The proposed CNN model demonstrates high accuracy and interpretability for ECG beat classification.
- This approach shows potential for automating cardiovascular diagnosis systems.
- The model can serve as a valuable tool for clinicians in diagnosing heart conditions.
Abstract:
Medical practitioners need to understand the critical features of ECG beats to diagnose and identify cardiovascular conditions accurately. This would be greatly facilitated by identifying the significant features of frequency components in temporal ECG wave-forms using computational methods. In this study, we have proposed a novel ECG beat classifier based on a customized VGG16-based Convolution Neural Network (CNN) that uses the time-frequency representation of temporal ECG, and a method to identify the contribution of interpretable ECG frequencies when classifying based on the SHapley Additive exPlanations (SHAP) values. We applied our model to the MIT-BIH arrhythmia dataset to classify the ECG beats and to characterise of the beats frequencies. This model was evaluated with two advanced time-frequency analysis methods. Our results indicated that for 2-4 classes our proposed model achieves a classification accuracy of 100% and for 5 classes it achieves a classification accuracy of 99.90%. We have also tested the proposed model using premature ventricular contraction beats from the American Heart Association (AHA) database and normal beats from Lobachevsky University Electrocardiography database (LUDB) and obtained a classification accuracy of 99.91% for the 5-classes case. In addition, SHAP value increased the interpretability of the ECG frequency features. Thus, this model could be applicable to the automation of the cardiovascular diagnosis system and could be used by clinicians.
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