Feature extraction and classification of ECG signals with support vector machines and particle swarm optimisation
| Autor: | Gandham Sreedevi, B. Anuradha |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
business.industry
Computer science Feature extraction Biomedical Engineering Particle swarm optimization Pattern recognition Support vector machine Svm classifier ComputingMethodologies_PATTERNRECOGNITION Discriminant function analysis Classifier (linguistics) Principal component analysis Artificial intelligence Ecg signal business |
| Zdroj: | International Journal of Biomedical Engineering and Technology. 35:242 |
| ISSN: | 1752-6426 1752-6418 |
| DOI: | 10.1504/ijbet.2021.113732 |
| Popis: | The present work was aimed to present a thorough experimental study that shows the superiority of the generalisation capability of the support vector machine (SVM) approach in the classification of electrocardiogram (ECG) signals. Feature extraction was done using principal component analysis (PCA). Further, a novel classification system based on particle swarm optimisation (PSO) was used to improve the generalisation performance of the SVM classifier. For this purpose, we have optimised the SVM classifier design by searching for the best value of the parameters that tune its discriminant function and upstream by looking for the best subset of features that feed the classifier. The obtained results clearly confirm the superiority of the SVM approach as compared to traditional classifiers, and suggest that further substantial improvements in terms of classification accuracy can be achieved by the proposed PSO-SVM classification system. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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