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Reconstruction of gastric slow wave from finger photoplethysmographic signal using radial basis function neural network

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Abstract

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.

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Acknowledgments

The authors sincerely acknowledge J. Rajkumar and A. Sricharan of Computational Neuroscience Group for their valuable contribution in this study. Authors would like to thank Department of Applied Mechanics of Indian Institute of Technology Madras for funding this study. The authors would like to thank all the volunteers who have participated in this study by sparing their valuable time and effort to make it successful.

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Authors and Affiliations

  1. Touch Lab, Biomedical Engineering Group, Department of Applied Mechanics, Indian Institute of Technology Madras, Chennai, 600036, Tamilnadu, India

    S. Mohamed Yacin & M. Manivannan

  2. Computational Neuroscience Lab, Department of Biotechnology, Indian Institute of Technology Madras, Chennai, 600036, Tamilnadu, India

    V. Srinivasa Chakravarthy

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  1. S. Mohamed Yacin
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  2. V. Srinivasa Chakravarthy
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  3. M. Manivannan
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Correspondence to M. Manivannan.

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Mohamed Yacin, S., Srinivasa Chakravarthy, V. & Manivannan, M. Reconstruction of gastric slow wave from finger photoplethysmographic signal using radial basis function neural network. Med Biol Eng Comput 49, 1241–1247 (2011). https://doi.org/10.1007/s11517-011-0796-1

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  • DOI: https://doi.org/10.1007/s11517-011-0796-1

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