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Comparison of Support-Vector Machine and Sparse Representation Using a Modified Rule-Based Method for Automated
Yi-Li Tseng1, Keng-Sheng Lin2, Fu-Shan Jaw3
1Department of Electrical Engineering, Fu Jen Catholic University, New Taipei City 24205, Taiwan; Institute of Biomedical Engineering, National Taiwan University, Taipei 10617, Taiwan.
Computational and Mathematical Methods in Medicine
|March 1, 2016
Summary
This study introduces an automatic method for detecting myocardial ischemia using ECG signals. Sparse representation-based classification (SRC) shows improved sensitivity over Support Vector Machines (SVM) for identifying abnormal heartbeats.
Area of Science:
- Cardiology
- Biomedical Engineering
- Signal Processing
Background:
- Myocardial ischemia is an early symptom of acute coronary events.
- ECG signals commonly show ST- and T-wave changes during ischemia.
- Accurate detection of abnormal ECG beats is crucial for early diagnosis.
Purpose of the Study:
- To develop and evaluate an automatic method for detecting myocardial ischemia.
- To compare the performance of Sparse Representation-based Classification (SRC) with Support Vector Machine (SVM) for ECG analysis.
- To improve the accuracy and efficiency of detecting abnormal heartbeats.
Main Methods:
- Utilized knowledge-based features and classification algorithms for ECG analysis.
- Implemented and compared Sparse Representation-based Classification (SRC) against Support Vector Machine (SVM).
- Employed rule-based vectors as the input feature space for both classification methods.
Main Results:
- SRC demonstrated higher sensitivity in detecting myocardial ischemia compared to SVM.
- A trade-off was observed between specificity and precision for the SRC method.
- SRC showed reduced dependence on feature selection, achieving high performance with fewer features.
Conclusions:
- The proposed SRC method offers a promising approach for automatic myocardial ischemia detection.
- SRC outperforms SVM in sensitivity for identifying abnormal ECG beats.
- The study highlights the potential of SRC for improving early diagnosis of cardiac events.
