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Arrhythmia detection by the graph convolution network and a proposed structure for communication between cardiac
Bahare Andayeshgar1, Fardin Abdali-Mohammadi2, Majid Sepahvand2
1Department of Biostatistics, School of Health, Kermanshah University of Medical Sciences, Kermanshah, 6715847141, Iran.
BMC Medical Research Methodology
|April 27, 2024
Summary
A new method using Graph Convolution Networks (GCN) and Weighted Mutual Information (WMI) significantly improves cardiac arrhythmia diagnosis accuracy by analyzing the structure of electrocardiogram (ECG) data. This approach achieved over 99% accuracy, outperforming existing methods.
Area of Science:
- Cardiology and Artificial Intelligence
- Biomedical Signal Processing
- Machine Learning in Healthcare
Background:
- Heart disease, particularly arrhythmia, is a leading global cause of mortality.
- Accurate and automated diagnosis of cardiac arrhythmia is crucial for timely intervention.
- Previous automated methods often overlook the structural relationships between electrocardiogram (ECG) leads.
Purpose of the Study:
- To introduce a novel structure for ECG data that incorporates relationships between heart leads.
- To develop an improved automated method for diagnosing and classifying cardiac arrhythmia using Graph Convolutional Networks (GCN).
- To enhance diagnostic accuracy by leveraging the structural information within ECG data.
Main Methods:
- A new ECG data structure was developed, utilizing Weighted Mutual Information (WMI) to quantify relationships between leads based on their electrical poles.
- Weighted Mutual Information matrices were generated using R software.
- A 15-layer Graph Convolutional Network (GCN) was trained and validated using the novel WMI structure on Chapman's 12-lead ECG Dataset.
Main Results:
- The proposed GCN-WMI network achieved high performance metrics: 98.74% sensitivity, 99.08% precision, 99.97% specificity, and 99.82% accuracy.
- The GCN-WMI model demonstrated superior accuracy compared to GCN-MI (99.71%) and GCN-Id (92.68%) on the same dataset.
- The developed method significantly outperformed previous studies on the Chapman dataset for arrhythmia diagnosis and classification.
Conclusions:
- The novel WMI-based ECG data structure effectively captures lead relationships, significantly improving GCN performance for arrhythmia detection.
- The GCN-WMI network represents a substantial advancement in the accuracy of automated cardiac arrhythmia diagnosis and classification.
- This approach offers a promising tool for clinical applications, enhancing the ability to identify and manage arrhythmias.
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