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Detection of coronary artery disease using multi-modal feature fusion and hybrid feature selection
Huan Zhang1, Xinpei Wang1, Changchun Liu1
1Institute of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, Shandong 250061, People's Republic of China.
Insights
This study developed advanced models for detecting coronary artery disease (CAD) by combining electrocardiogram (ECG) and phonocardiogram (PCG) signals with Holter monitoring, echocardiography (ECHO), and biomarker (BIO) data. The best model achieved 96.67% accuracy, improving CAD diagnosis.
Area of Science:
- Cardiology
- Biomedical Engineering
- Data Science
Background:
- Coronary artery disease (CAD) is a leading cause of mortality, necessitating improved diagnostic methods.
- Current screening for CAD requires more accurate and efficient tools to aid clinical decision-making.
Purpose of the Study:
- To develop optimal multi-modal detection models for suspected CAD patients.
- To integrate various physiological signals and clinical data for enhanced diagnostic accuracy.
Main Methods:
- Collected electrocardiogram (ECG) and phonocardiogram (PCG) signals from CAD patients and controls.
- Extracted multi-domain features from ECG and PCG, alongside Holter monitoring, echocardiography (ECHO), and biomarker (BIO) data.
- Employed a hybrid feature selection (HFS) method and support vector machine (SVM) with nested cross-validation for classification.
Main Results:
- The Holter monitoring model showed the highest single-modal accuracy at 82.67%.
- Multi-modal models demonstrated superior performance, with the five-modal ECG-PCG-Holter-ECHO-BIO model achieving 96.67% accuracy.
- The optimal five-modal model yielded 96.67% accuracy, sensitivity, and specificity.
Conclusions:
- Multi-modal feature fusion significantly enhances information for CAD detection.
- The developed HFS method and integrated models offer a valuable reference for physicians in diagnosing CAD.
Abstract:
Objective: Coronary artery disease (CAD) is a common fatal disease. At present, an accurate method to screen CAD is urgently needed. This study aims to provide optimal detection models for suspected CAD detection according to the differences in medical conditions, so as to assist physicians to make accurate judgments on suspected CAD patients.Approach: Electrocardiogram (ECG) and phonocardiogram (PCG) signals of 32 CAD patients and 30 patients with chest pain and normal coronary angiograms (CPNCA) were simultaneously collected for this paper. For each subject, the ECG and PCG multi-domain features were extracted, and the results of Holter monitoring, echocardiography (ECHO), and biomarker levels (BIO) were obtained to construct a multi-modal feature set. Then, a hybrid feature selection (HFS) method was developed using mutual information, recursive feature elimination, random forest, and weight of support vector machine to obtain the optimal feature subset. A support vector machine with nested cross-validation was used for classification.Main results: Results showed that the Holter model achieved the best performance as a single-modal feature model with an accuracy of 82.67%. In terms of multi-modal feature models, PCG-Holter, PCG-Holter-ECHO, PCG-Holter-ECHO-BIO, and ECG-PCG-Holter-ECHO-BIO were the optimal bimodal, three-modal, four-modal, and five-modal models, with accuracies of 90.38%, 91.92%, 95.25%, and 96.67%, respectively. Among them, the ECG-PCG-Holter-ECHO-BIO model, which was constructed by combining ECG and PCG signals features with Holter, ECHO, and BIO examination results, achieved the best classification results with an average accuracy, sensitivity, specificity, and F1-measure of 96.67%, 96.67%, 96.67%, and 96.64%, respectively.Significance: The study indicated that multi-modal feature fusion and HFS can obtain more effective information for CAD detection and provide a reference for physicians to diagnose CAD patients.
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