DNN-BP: a novel framework for cuffless blood pressure measurement from optimal PPG features using deep learning model.
| Publicado en: | Medical & Biological Engineering & Computing Vol. 62; no. 12; pp. 3687 - 3709 |
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| Autores principales: | , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Dec2024
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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=180936623&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 180936623 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Dec2024 vid: 62 iid: 12 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 180936623 178266154 10.1007/s11517-024-03157-1 180936623 ppf: 3687 ppct: 22 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: DNN-BP: a novel framework for cuffless blood pressure measurement from optimal PPG features using deep learning model. aug: au: Raju, S. M. Taslim Uddin Dipto, Safin Ahmed Hossain, Md Imran Chowdhury, Md. Abu Shahid Haque, Fabliha Nashrah, Ayesha Tun Nishan, Araf Khan, Md Mahamudul Hasan Hashem, M. M. A. affil: https://ror.org/04y58d606 Department of Computer Science and Engineering, Khulna University of Engineering & Technology, 9203, Khulna, Bangladesh sug: pubtype: Academic Journal doctype: Journal Article ougenre: Article ab: language: English refInfo: holdings: @attributes: islocal: N |
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