Automated Signal Quality Assessment for Heart Sound Signal by Novel Features and Evaluation in Open Public Datasets.
Automated heart sound signal quality assessment is a necessary step for reliable analysis of heart sound signal. An unavoidable processing step for this objective is the heart sound segmentation, which is still a challenging task from a technical viewpoint. In this study, ten features are defined to...
| Publicado en: | BioMed Research International pp. 1 - 16 |
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| Autores principales: | , , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
2/24/2021
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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=148931381&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 148931381 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 2/24/2021 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 148931381 148931381 148931381 10.1155/2021/7565398 148931381 ppf: 1 ppct: 15 formats: fmt: @attributes: type: P tig: atl: Automated Signal Quality Assessment for Heart Sound Signal by Novel Features and Evaluation in Open Public Datasets. aug: au: Tang, Hong Wang, Miao Hu, Yating Guo, Binbin Li, Ting affil: School of Biomedical Engineering, Dalian University of Technology, Dalian 116024, China sug: subj: Heart Sounds Signal Processing, Computer Assisted Evaluation Human Algorithms Heart Auscultation Cardiac Patients Descriptive Statistics ab: Automated heart sound signal quality assessment is a necessary step for reliable analysis of heart sound signal. An unavoidable processing step for this objective is the heart sound segmentation, which is still a challenging task from a technical viewpoint. In this study, ten features are defined to evaluate the quality of heart sound signal without segmentation. The ten features come from kurtosis, energy ratio, frequency-smoothed envelope, and degree of sound periodicity, where five of them are novel in signal quality assessment. We have collected a total of 7893 recordings from open public heart sound databases and performed manual annotation for each recording as gold standard quality label. The signal quality is classified based on two schemes: binary classification ("unacceptable" and "acceptable") and triple classification ("unacceptable", "good," and "excellent"). Sequential forward feature selection shows that the feature "the degree of periodicity" gives an accuracy rate of 73.1% in binary SVM classification. The top five features dominate the classification performance and give an accuracy rate of 92%. The binary classifier has excellent generalization ability since the accuracy rate reaches to (90.4 ± 0.5) % even if 10% of the data is used to train the classifier. The rate increases to (94.3 ± 0.7) % in 10-fold validation. The triple classification has an accuracy rate of (85.7 ± 0.6) % in 10-fold validation. The results verify the effectiveness of the signal quality assessment, which could serve as a potential candidate as a preprocessing in future automatic heart sound analysis in clinical application. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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