Classification of heart sounds based on the combination of the modified frequency wavelet transform and convolutional neural network.
We purpose a novel method that combines modified frequency slice wavelet transform (MFSWT) and convolutional neural network (CNN) for classifying normal and abnormal heart sounds. A hidden Markov model is used to find the position of each cardiac cycle in the heart sound signal and determine the exa...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 58; no. 9; pp. 2039 - 2048 |
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| Autores principales: | , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Sep2020
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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=145048082&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 145048082 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Sep2020 vid: 58 iid: 9 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 145048082 144444153 145048082 NLM32638275 10.1007/s11517-020-02218-5 NLM32638275 145048082 ppf: 2039 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Classification of heart sounds based on the combination of the modified frequency wavelet transform and convolutional neural network. aug: au: Chen, Yongchao Wei, Shoushui Zhang, Yatao affil: School of Control Science and Engineering, Shandong University, Jinan, China sug: subj: Signal Processing, Computer Assisted Heart Sounds Physiology Models, Biological Algorithms Probability Cardiovascular Diseases Diagnosis Biomedical Engineering Diagnosis, Computer Assisted Statistics and Numerical Data Heart Auscultation Statistics and Numerical Data Diagnosis, Computer Assisted Methods Cardiovascular Diseases Physiopathology ab: We purpose a novel method that combines modified frequency slice wavelet transform (MFSWT) and convolutional neural network (CNN) for classifying normal and abnormal heart sounds. A hidden Markov model is used to find the position of each cardiac cycle in the heart sound signal and determine the exact position of the four periods of S1, S2, systole, and diastole. Then the one-dimensional cardiac cycle signal was converted into a two-dimensional time-frequency picture using the MFSWT. Finally, two CNN models are trained using the aforementioned pictures. We combine two CNN models using sample entropy (SampEn) to determine which model is used to classify the heart sound signal. We evaluated our model on the heart sound public dataset provided by the PhysioNet Computing in Cardiology Challenge 2016. Experimental classification performance from a 10-fold cross-validation indicated that sensitivity (Se), specificity (Sp) and mean accuracy (MAcc) were 0.95, 0.93, and 0.94, respectively. The results showed the proposed method can classify normal and abnormal heart sounds with efficiency and high accuracy. Graphical abstract Block diagram of heart sound classification. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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