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Published on: December 11, 2019
A hybrid deep learning network for automatic diagnosis of cardiac arrhythmia based on 12-lead ECG
Xiangyun Bai1, Xinglong Dong2, Yabing Li2
1School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, Xi'an, 710121, China. baixiangyun@xupt.edu.cn.
Insights
A new hybrid deep learning model accurately detects cardiac arrhythmias from electrocardiography (ECG) signals. This advanced system aids clinicians in real-time diagnosis, improving patient outcomes for cardiovascular diseases.
Area of Science:
- Cardiology
- Biomedical Engineering
- Artificial Intelligence
Background:
- Cardiac arrhythmias are a major global health concern, leading to significant mortality and economic burden.
- Electrocardiography (ECG) is a vital, non-invasive diagnostic tool for cardiovascular diseases.
- Traditional ECG analysis is subjective, time-consuming, and relies heavily on expert interpretation.
Purpose of the Study:
- To develop and validate a novel hybrid deep learning model for automated cardiac arrhythmia detection.
- To enhance the accuracy and efficiency of ECG interpretation for clinical use.
Main Methods:
- A hybrid deep learning model, the CBGM model, integrating Convolutional Neural Network (CNN) and Bidirectional Gated Recurrent Unit (BiGRU) with multi-head attention was proposed.
- The CNN component utilized seven convolutional layers with varying filter sizes and three pooling layers.
- The BiGRU module comprised two layers with 64 units each, followed by 8-head multi-head attention to capture spatio-temporal features and global correlations in ECG signals.
Main Results:
- The CBGM model achieved high performance on the MIT-BIH arrhythmia database: 99.41% accuracy, 99.15% precision, 99.68% specificity, and 99.21% F1-Score.
- Validation on the PTB Diagnostic ECG Database yielded an accuracy of 98.82%, demonstrating strong generalization capability.
- Comparative analysis confirmed superior performance over existing methods in automatic arrhythmia classification.
Conclusions:
- The proposed CBGM model offers a robust and highly accurate solution for automatic cardiac arrhythmia classification.
- This deep learning approach can significantly assist clinicians by enabling real-time detection of arrhythmias during routine ECG screenings.
- The model's effectiveness holds promise for improving the management and prognosis of cardiovascular diseases globally.
Abstract:
Cardiac arrhythmias are the leading cause of death and pose a huge health and economic burden globally. Electrocardiography (ECG) is an effective technique for the diagnosis of cardiovascular diseases because of its noninvasive and cost-effective advantages. However, traditional ECG analysis relies heavily on the clinical experience of physicians, which can be challenging and time-consuming to produce valid diagnostic results. This work proposes a new hybrid deep learning model that combines convolutional neural network (CNN) and bidirectional gated recurrent unit (BiGRU) with multi-head attention (CBGM model). Specifically, the model consists of seven convolutional layers with varying filter sizes (4, 16, 32, and 64) and three pooling layers, respectively, while the BiGRU module includes two layers with 64 units each followed by multi-head attention (8-heads). The combination of CNN and BiGRU effectively captures spatio-temporal features of ECG signals, with multi-head attention comprehensively extracted global correlations among multiple segments of ECG signals. The validation in the MIT-BIH arrhythmia database achieved an accuracy of 99.41%, a precision of 99.15%, a specificity of 99.68%, and an F1-Score of 99.21%, indicating its robust performance across different evaluation metrics. Additionally, the model's performance was evaluated on the PTB Diagnostic ECG Database, where it achieved an accuracy of 98.82%, demonstrating its generalization capability. Comparative analysis against previous methods revealed that our proposed CBGM model exhibits more higher performance in automatic classification of arrhythmia and can be helpful for assisting clinicians by enabling real-time detection of cardiac arrhythmias during routine ECG screenings.
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Definition
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