An Automatic Approach Using ELM Classifier for HFpEF Identification Based on Heart Sound Characteristics.
Heart failure with preserved ejection fraction (HFpEF) is a complex and heterogeneous clinical syndrome. For the purpose of assisting HFpEF diagnosis, a non-invasive method using extreme learning machine and heart sound (HS) characteristics was provided in this paper. Firstly, the improved wavelet d...
| Publicado en: | Journal of Medical Systems Vol. 43; no. 9 |
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| Autores principales: | , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Sep2019
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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=138200102&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 138200102 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Sep2019 vid: 43 iid: 9 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 138200102 138200102 138200102 10.1007/s10916-019-1415-1 138200102 ppct: 1 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: An Automatic Approach Using ELM Classifier for HFpEF Identification Based on Heart Sound Characteristics. aug: au: Liu, Yongmin Guo, Xingming Zheng, Yineng affil: Key Laboratory of Biorheology Science and Technology, Ministry of Education, College of Bioengineering, Chongqing University, 400044, Chongqing, China sug: subj: Heart Failure Diagnosis Ventricular Ejection Fraction Physiology Machine Learning Methods Heart Sounds Physiology Noninvasive Procedures Methods Human Comparative Studies Heart Failure Physiopathology Image Processing, Computer Assisted Logistic Regression Algorithms Sensitivity and Specificity T-Tests Data Analysis Software Funding Source ab: Heart failure with preserved ejection fraction (HFpEF) is a complex and heterogeneous clinical syndrome. For the purpose of assisting HFpEF diagnosis, a non-invasive method using extreme learning machine and heart sound (HS) characteristics was provided in this paper. Firstly, the improved wavelet denoising method was used for signal preprocessing. Then, the logistic regression based hidden semi-Markov model algorithm was utilized to locate the boundary of the first HS and the second HS, therefore, the ratio of diastolic to systolic duration can be calculated. Eleven features were extracted based on multifractal detrended fluctuation analysis to analyze the differences of multifractal behavior of HS between healthy people and HFpEF patients. Afterwards, the statistical analysis was implemented on the extracted HS characteristics to generate the diagnostic feature set. Finally, the extreme learning machine was applied for HFpEF identification by the comparison of performances with support vector machine. The result shows an accuracy of 96.32%, a sensitivity of 95.48% and a specificity of 97.10%, which demonstrates the effectiveness of HS for HFpEF diagnosis. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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