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Updated: Jun 29, 2025
Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice
Published on: May 23, 2021
ECG-based cardiac arrhythmias detection through ensemble learning and fusion of deep spatial-temporal and long-range
Sadia Din1, Marwa Qaraqe2, Omar Mourad3
1Texas A&M University, Electrical and Computer Engineering Program, Doha, Qatar.
Insights
Early diagnosis of heart arrhythmia using electrocardiograms (ECG) is vital. This study introduces a novel deep learning model combining CNN, LSTM, and Transformer for accurate arrhythmia detection, achieving 99.56% accuracy.
Area of Science:
- Cardiology
- Artificial Intelligence
- Biomedical Signal Processing
Background:
- Cardiac arrhythmia is a leading global cause of death, necessitating early diagnosis through electrocardiogram (ECG) analysis.
- Manual ECG interpretation is time-consuming and prone to errors, highlighting the need for automated detection methods.
- Existing deep learning models (CNN, LSTM, Transformer) individually struggle to capture diverse ECG signal features.
Purpose of the Study:
- To develop a comprehensive deep learning model for enhanced automated detection of cardiac arrhythmias.
- To overcome the limitations of individual deep learning models in capturing complex ECG signal characteristics.
- To improve the accuracy and reliability of arrhythmia diagnosis through advanced feature fusion.
Main Methods:
- A novel hybrid deep learning architecture merging Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Transformer models was proposed.
- The fusion technique aimed to extract spatial, temporal, and long-range dependency features from ECG signals.
- A majority voting classifier, enhanced with deep features from traditional base learners, was employed for final classification.
Main Results:
- The proposed CNN-LSTM-Transformer fusion model demonstrated superior performance compared to existing state-of-the-art models.
- The model achieved a high accuracy of 99.56% in detecting cardiac arrhythmias on the MIT-BIH database.
- The integrated approach effectively captured diverse ECG signal attributes, leading to improved diagnostic performance.
Conclusions:
- The proposed comprehensive feature fusion technique significantly enhances cardiac arrhythmia detection accuracy.
- Combining CNN, LSTM, and Transformer models offers a robust solution for analyzing complex ECG data.
- This advanced deep learning approach holds promise for improving early diagnosis and management of heart arrhythmias.
Abstract:
Cardiac arrhythmia is one of the prime reasons for death globally. Early diagnosis of heart arrhythmia is crucial to provide timely medical treatment. Heart arrhythmias are diagnosed by analyzing the electrocardiogram (ECG) of patients. Manual analysis of ECG is time-consuming and challenging. Hence, effective automated detection of heart arrhythmias is important to produce reliable results. Different deep-learning techniques to detect heart arrhythmias such as Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Transformer, and Hybrid CNN-LSTM were proposed. However, these techniques, when used individually, are not sufficient to effectively learn multiple features from the ECG signal. The fusion of CNN and LSTM overcomes the limitations of CNN in the existing studies as CNN-LSTM hybrids can extract spatiotemporal features. However, LSTMs suffer from long-range dependency issues due to which certain features may be ignored. Hence, to compensate for the drawbacks of the existing models, this paper proposes a more comprehensive feature fusion technique by merging CNN, LSTM, and Transformer models. The fusion of these models facilitates learning spatial, temporal, and long-range dependency features, hence, helping to capture different attributes of the ECG signal. These features are subsequently passed to a majority voting classifier equipped with three traditional base learners. The traditional learners are enriched with deep features instead of handcrafted features. Experiments are performed on the MIT-BIH arrhythmias database and the model performance is compared with that of the state-of-art models. Results reveal that the proposed model performs better than the existing models yielding an accuracy of 99.56%.
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