An acoustical respiratory phase segmentation algorithm using genetic approach.
This paper proposes a robust and fully automated respiratory phase segmentation method using single channel tracheal breath sounds (TBS) recordings of different types. The estimated number of respiratory segments in a TBS signal is firstly obtained based on noise estimation and nonlinear mapping. Re...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 47; no. 9; pp. 941 - 954 |
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| Autores principales: | , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Sep2009
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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=104907041&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104907041 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Sep2009 vid: 47 iid: 9 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104907041 NLM19639357 2010382766 10.1007/s11517-009-0518-0 NLM19639357 104907041 ppf: 941 ppct: 13 formats: fmt: @attributes: type: P tig: atl: An acoustical respiratory phase segmentation algorithm using genetic approach. aug: au: Jin F Sattar F Goh DY Jin, F Sattar, F Goh, D Y T affil: School of Electrical and Electronic Engineering, Nanyang Technological University, Nanyang Avenue 50, Singapore 639798, Singapore sug: subj: Diagnosis, Computer Assisted Respiration Algorithms Respiratory Sounds Sound Spectrography Methods ab: This paper proposes a robust and fully automated respiratory phase segmentation method using single channel tracheal breath sounds (TBS) recordings of different types. The estimated number of respiratory segments in a TBS signal is firstly obtained based on noise estimation and nonlinear mapping. Respiratory phase boundaries are then located through the generations of multi-population genetic algorithm by introducing a new evaluation function based on sample entropy (SampEn) and a heterogeneity measure. The performance of the proposed method is analyzed for single channel TBS recordings of various types. An overall respiratory phase segmentation accuracy is found to be 12 +/- 5 ms for normal TBS and 21 +/- 9 ms for adventitious sounds. The results show the robustness and effectiveness of the proposed segmentation method. The proposed method has been a successful attempt to solve the clinical application challenge faced by the existing phase segmentation methods in terms of respiratory dysfunctions. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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