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[Automatic Identifcation of Heart Block Precise Location Based on Sparse Connection Residual Network]
Ji Qi1,2, Ruiqing Zhang1, Yang Shen1
1Department of Biomedical Engineering, China Medical University, Shenyang, 110122.
Summary
A novel sparse connection residual network accurately classifies electrocardiogram (ECG) signals for Right Bundle Branch Block (RBBB) and Left Bundle Branch Block (LBBB) with 95.2% accuracy, aiding clinical diagnosis.
Area of Science:
- Cardiology
- Biomedical Engineering
- Artificial Intelligence in Medicine
Background:
- Electrocardiogram (ECG) analysis is crucial for diagnosing cardiac conditions.
- Automated classification of ECG signals can improve diagnostic efficiency.
- Distinguishing between normal ECGs, Right Bundle Branch Block (RBBB), and Left Bundle Branch Block (LBBB) is clinically significant.
Purpose of the Study:
- To develop an automated method for classifying normal ECG signals, RBBB, and LBBB.
- To introduce a novel sparse connection residual network algorithm for ECG classification.
Main Methods:
- Utilized the MIT-BIH database for training and testing.
- Developed a convolutional neural network-based algorithm: sparse connection residual network.
- Compared the proposed network's performance against classic network models.
Main Results:
- Achieved a test set accuracy of 95.2% on the MIT-BIH database.
- Demonstrated superior performance compared to traditional network models.
- The sparse connection residual network effectively recognized ECG signal patterns.
Conclusions:
- The proposed algorithm shows high potential for assisting clinicians in diagnosing heart block-related diseases.
- The developed method offers significant clinical application value.
- Automated ECG classification using advanced neural networks can enhance diagnostic capabilities.
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