An automatic method for arterial pulse waveform recognition using KNN and SVM classifiers.
The measurement and analysis of the arterial pulse waveform (APW) are the means for cardiovascular risk assessment. Optical sensors represent an attractive instrumental solution to APW assessment due to their truly non-contact nature that makes the measurement of the skin surface displacement possib...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 54; no. 7; pp. 1049 - 1060 |
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| Autores principales: | , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Jul2016
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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=116194410&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 116194410 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Jul2016 vid: 54 iid: 7 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 116194410 116194410 NLM26403299 10.1007/s11517-015-1393-5 NLM26403299 116194410 ppf: 1049 ppct: 11 formats: fmt: @attributes: type: P tig: atl: An automatic method for arterial pulse waveform recognition using KNN and SVM classifiers. aug: au: Pereira, Tânia Paiva, Joana Correia, Carlos Cardoso, João Pereira, Tânia Paiva, Joana S Cardoso, João affil: Physics Department, Instrumentation Center, University of Coimbra, Rua Larga 3004-516 Coimbra Portugal sug: subj: Signal Processing, Computer Assisted Pulse Methods Optics Equipment and Supplies Optics Methods Equipment Design Arteries Scales ab: The measurement and analysis of the arterial pulse waveform (APW) are the means for cardiovascular risk assessment. Optical sensors represent an attractive instrumental solution to APW assessment due to their truly non-contact nature that makes the measurement of the skin surface displacement possible, especially at the carotid artery site. In this work, an automatic method to extract and classify the acquired data of APW signals and noise segments was proposed. Two classifiers were implemented: k-nearest neighbours and support vector machine (SVM), and a comparative study was made, considering widely used performance metrics. This work represents a wide study in feature creation for APW. A pool of 37 features was extracted and split in different subsets: amplitude features, time domain statistics, wavelet features, cross-correlation features and frequency domain statistics. The support vector machine recursive feature elimination was implemented for feature selection in order to identify the most relevant feature. The best result (0.952 accuracy) in discrimination between signals and noise was obtained for the SVM classifier with an optimal feature subset . pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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