Reconstruction of gastric slow wave from finger photoplethysmographic signal using radial basis function neural network.
Extraction of extra-cardiac information from photoplethysmography (PPG) signal is a challenging research problem with significant clinical applications. In this study, radial basis function neural network (RBFNN) is used to reconstruct the gastric myoelectric activity (GMA) slow wave from finger PPG...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 49; no. 11; pp. 1241 - 1248 |
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| Autores principales: | , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Nov2011
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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=104595288&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104595288 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Nov2011 vid: 49 iid: 11 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104595288 NLM21748397 2011349646 10.1007/s11517-011-0796-1 NLM21748397 104595288 ppf: 1241 ppct: 7 formats: fmt: @attributes: type: P tig: atl: Reconstruction of gastric slow wave from finger photoplethysmographic signal using radial basis function neural network. aug: au: Mohamed Yacin S Srinivasa Chakravarthy V Manivannan M Mohamed Yacin, S Srinivasa Chakravarthy, V Manivannan, M affil: Touch Lab, Biomedical Engineering Group, Department of Applied Mechanics, Indian Institute of Technology Madras, Chennai, 600036, Tamilnadu, India sug: subj: Neural Networks (Computer) Plethysmography Methods Stomach Physiology Adult Fingers Physiology Gastrointestinal Motility Physiology Male Signal Processing, Computer Assisted Young Adult Adult: 19-44 years Male ab: Extraction of extra-cardiac information from photoplethysmography (PPG) signal is a challenging research problem with significant clinical applications. In this study, radial basis function neural network (RBFNN) is used to reconstruct the gastric myoelectric activity (GMA) slow wave from finger PPG signal. Finger PPG and GMA (measured using Electrogastrogram, EGG) signals were acquired simultaneously at the sampling rate of 100 Hz from ten healthy subjects. Discrete wavelet transform (DWT) was used to extract slow wave (0-0.1953 Hz) component from the finger PPG signal; this slow wave PPG was used to reconstruct EGG. A RBFNN is trained on signals obtained from six subjects in both fasting and postprandial conditions. The trained network is tested on data obtained from the remaining four subjects. In the earlier study, we have shown the presence of GMA information in finger PPG signal using DWT and cross-correlation method. In this study, we explicitly reconstruct gastric slow wave from finger PPG signal by the proposed RBFNN-based method. It was found that the network-reconstructed slow wave provided significantly higher (P < 0.0001) correlation (≥ 0.9) with the subject's EGG slow wave than the correlation obtained (≈0.7) between the PPG slow wave from DWT and the EEG slow wave. Our results showed that a simple finger PPG signal can be used to reconstruct gastric slow wave using RBFNN method. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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