Med Biol Eng Comput - Reconstruction of gastric slow wave from finger photoplethysmographic signal using radial basis function neural network.

Tópicos

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Resumo

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.

Resumo Limpo

extract extracardiac inform photoplethysmographi ppg signal challeng research problem signific clinic applic studi radial basi function neural network rbfnn use reconstruct gastric myoelectr activ gma slow wave finger ppg signal finger ppg gma measur use electrogastrogram egg signal acquir simultan sampl rate hz ten healthi subject discret wavelet transform dwt use extract slow wave hz compon finger ppg signal slow wave ppg use reconstruct egg rbfnn train signal obtain six subject fast postprandi condit train network test data obtain remain four subject earlier studi shown presenc gma inform finger ppg signal use dwt crosscorrel method studi explicit reconstruct gastric slow wave finger ppg signal propos rbfnnbase method found networkreconstruct slow wave provid signific higher p correl subject egg slow wave correl obtain ppg slow wave dwt eeg slow wave result show simpl finger ppg signal can use reconstruct gastric slow wave use rbfnn method

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