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Automated diagnosis of coronary artery disease (CAD) patients using optimized SVM
Azam Davari Dolatabadi1, Siamak Esmael Zadeh Khadem1, Babak Mohammadzadeh Asl2
1Mechanical Engineering Department, Tarbiat Modares University, Tehran, Iran.
Insights
This study introduces an automated method for diagnosing Coronary Artery Disease (CAD) using Heart Rate Variability (HRV) from ECG signals, achieving high accuracy. The approach offers a non-invasive and effective tool for cardiovascular health assessment.
Area of Science:
- Biomedical Engineering
- Cardiology
- Machine Learning
Background:
- Coronary Artery Disease (CAD) is a leading cause of death and disability.
- Current diagnostic methods for CAD are often invasive and lack sufficient accuracy.
Purpose of the Study:
- To develop an automated diagnostic methodology for Coronary Artery Disease (CAD).
- To utilize Heart Rate Variability (HRV) signals from electrocardiograms (ECG) for non-invasive CAD detection.
Main Methods:
- Feature extraction from HRV signals in time, frequency, and nonlinear domains.
- Dimensionality reduction using Principal Component Analysis (PCA).
- Classification using an optimized Support Vector Machine (SVM) classifier.
Main Results:
- The proposed algorithm achieved 99.2% accuracy in detecting CAD.
- Sensitivity of 98.43% and specificity of 100% were reported for CAD detection.
Conclusions:
- Feature extraction from biomedical signals is a viable approach for health status prediction.
- Automated HRV analysis using machine learning offers a promising tool for non-invasive CAD diagnosis.
Background And Objective:
Currently Coronary Artery Disease (CAD) is one of the most prevalent diseases, and also can lead to death, disability and economic loss in patients who suffer from cardiovascular disease. Diagnostic procedures of this disease by medical teams are typically invasive, although they do not satisfy the required accuracy.
Methods:
In this study, we have proposed a methodology for the automatic diagnosis of normal and Coronary Artery Disease conditions using Heart Rate Variability (HRV) signal extracted from electrocardiogram (ECG). The features are extracted from HRV signal in time, frequency and nonlinear domains. The Principal Component Analysis (PCA) is applied to reduce the dimension of the extracted features in order to reduce computational complexity and to reveal the hidden information underlaid in the data. Finally, Support Vector Machine (SVM) classifier has been utilized to classify two classes of data using the extracted distinguishing features. In this paper, parameters of the SVM have been optimized in order to improve the accuracy.
Results:
Provided reports in this paper indicate that the detection of CAD class from normal class using the proposed algorithm was performed with accuracy of 99.2%, sensitivity of 98.43%, and specificity of 100%.
Conclusions:
This study has shown that methods which are based on the feature extraction of the biomedical signals are an appropriate approach to predict the health situation of the patients.
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