PCG Classification Using Multidomain Features and SVM Classifier
| Autor: | Ziyin Dai, Hong Tang, Ting Li, Chengyu Liu, Yuanlin Jiang |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Support Vector Machine
Databases Factual Article Subject Computer science Entropy 0206 medical engineering lcsh:Medicine 02 engineering and technology Sensitivity and Specificity General Biochemistry Genetics and Molecular Biology Svm classifier Cepstrum 0202 electrical engineering electronic engineering information engineering Entropy (information theory) Humans General Immunology and Microbiology business.industry Small number lcsh:R Pattern recognition General Medicine 020601 biomedical engineering Frequency spectrum Support vector machine Heart Sounds Heart sounds Correlation analysis 020201 artificial intelligence & image processing Artificial intelligence business Algorithms Research Article |
| Zdroj: | BioMed Research International BioMed Research International, Vol 2018 (2018) |
| ISSN: | 2314-6133 |
| DOI: | 10.1155/2018/4205027 |
| Popis: | This paper proposes a method using multidomain features and support vector machine (SVM) for classifying normal and abnormal heart sound recordings. The database was provided by the PhysioNet/CinC Challenge 2016. A total of 515 features are extracted from nine feature domains, i.e., time interval, frequency spectrum of states, state amplitude, energy, frequency spectrum of records, cepstrum, cyclostationarity, high-order statistics, and entropy. Correlation analysis is conducted to quantify the feature discrimination abilities, and the results show that “frequency spectrum of state”, “energy”, and “entropy” are top domains to contribute effective features. A SVM with radial basis kernel function was trained for signal quality estimation and classification. The SVM classifier is independently trained and tested by many groups of top features. It shows the average of sensitivity, specificity, and overall score are high up to 0.88, 0.87, and 0.88, respectively, when top 400 features are used. This score is competitive to the best previous scores. The classifier has very good performance with even small number of top features for training and it has stable output regardless of randomly selected features for training. These simulations demonstrate that the proposed features and SVM classifier are jointly powerful for classifying heart sound recordings. |
| Databáze: | OpenAIRE |
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