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Electrocardiogram Signal Classification in the Diagnosis of Heart Disease Based on RBF Neural Network
Yan Fang1,2,3, Jianshe Shi4, Yifeng Huang5
1Faculty of Mathematics and Computer Science, Quanzhou Normal University, Quanzhou 362000, China.
Insights
This study introduces an automated method for heart disease detection using electrocardiogram (ECG) signals. The developed system achieves high accuracy in identifying ECG abnormalities, aiding in efficient heart condition diagnosis.
Area of Science:
- Biomedical Engineering
- Cardiology
- Artificial Intelligence in Medicine
Background:
- Heart disease diagnosis relies heavily on electrocardiogram (ECG) interpretation.
- Current manual ECG analysis by medical staff is time-consuming and inefficient.
- Automated ECG analysis offers a solution to improve diagnostic efficiency and reduce workload.
Purpose of the Study:
- To develop an automated system for accurate heart disease detection using ECG signals.
- To evaluate the effectiveness of the Pan-Tompkins algorithm and RBF neural network for ECG analysis.
- To improve the efficiency and accuracy of diagnosing heart conditions.
Main Methods:
- Utilized the MIT-BIH ECG database for signal analysis.
- Applied the Pan-Tompkins algorithm to extract QRS features from ECG signals.
- Employed K-means clustering for sample selection and Radial Basis Function (RBF) neural network for classification.
Main Results:
- The automated system achieved a classification accuracy of 98.9%.
- The method demonstrated effectiveness in detecting ECG signal abnormalities.
- Successful implementation for heart disease diagnosis was shown.
Conclusions:
- The proposed automated ECG analysis method is highly accurate and effective for heart disease diagnosis.
- This approach can significantly assist medical professionals in diagnosing heart conditions.
- The integration of signal processing and machine learning enhances ECG interpretation capabilities.
Abstract:
Heart disease is a common disease affecting human health. Electrocardiogram (ECG) classification is the most effective and direct method to detect heart disease, which is helpful to the diagnosis of most heart disease symptoms. At present, most ECG diagnosis depends on the personal judgment of medical staff, which leads to heavy burden and low efficiency of medical staff. Automatic ECG analysis technology will help the work of relevant medical staff. In this paper, we use the MIT-BIH ECG database to extract the QRS features of ECG signals by using the Pan-Tompkins algorithm. After extraction of the samples, K-means clustering is used to screen the samples, and then, RBF neural network is used to analyze the ECG information. The classifier trains the electrical signal features, and the classification accuracy of the final classification model can reach 98.9%. Our experiments show that this method can effectively detect the abnormality of ECG signal and implement it for the diagnosis of heart disease.
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