PCG Classification Using Multidomain Features and SVM Classifier.
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, f...
| Publicado en: | BioMed Research International Vol. 2018; pp. 1 - 15 |
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| Autores principales: | , , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
7/9/2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=130581080&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 130581080 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 7/9/2018 vid: 2018 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 130581080 130581080 130581080 10.1155/2018/4205027 130581080 ppf: 1 ppct: 14 formats: fmt: @attributes: type: P tig: atl: PCG Classification Using Multidomain Features and SVM Classifier. aug: au: Tang, Hong Dai, Ziyin Jiang, Yuanlin Li, Ting Liu, Chengyu affil: Department of Biomedical Engineering, Dalian University of Technology, Dalian, China sug: subj: Heart Auscultation Classification Machine Learning Human Time Factors Spectrum Analysis Mathematics Data Analysis, Statistical Physics Sensitivity and Specificity Simulations ab: 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. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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