This article investigates the possibility of extracting gastric motility (GM) information from finger photoplethysmographic (PPG) signals non-invasively. Now-a-days measuring GM is a challenging task because of invasive and complicated clinical procedures involved. It is well-known that the PPG signal acquired from finger consists of information related to heart rate and respiratory rate. This thread is taken further and effort has been put here to find whether it is possible to extract GM information from finger PPG in an easier way and without discomfort to the patients. Finger PPG and GM (measured using Electrogastrogram, EGG) signals were acquired simultaneously at the rate of 100 Hz from eight healthy subjects for 30 min duration in fasting and postprandial states. In this study, we process the finger PPG signal and extract a slow wave that is analogous to actual EGG signal. To this end, we chose two advanced signal processing approaches: first, we perform discrete wavelet transform (DWT) to separate the different components, since PPG and EGG signals are non-stationary in nature. Second, in the frequency domain, we perform cross-spectral and coherence analysis using autoregressive (AR) spectral estimation method in order to compare the spectral details of recorded PPG and EGG signals. In DWT, a lower frequency oscillation (≈0.05 Hz) called slow wave was extracted from PPG signal which looks similar to the slow wave of GM in both shape and frequency in the range (0-0.1953) Hz. Comparison of these two slow wave signals was done by normalized cross-correlation technique. Cross-correlation values are found to be high (range 0.68-0.82, SD 0.12, R = 1.0 indicates exact agreement, p < 0.05) for all subjects and there is no significant difference in cross-correlation between fasting and postprandial states. The coherence analysis results demonstrate that a moderate coherence (range 0.5-0.7, SD 0.13, p < 0.05) exists between EGG and PPG signal in the "slow wave" frequency band, without any significant change in the level of coherence in postprandial state. These results indicate that finger PPG signal contains GM-related information. The findings are sufficiently encouraging to motivate further exploration of finger PPG as a non-invasive source of GM-related information.

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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In this article we investigate the presence of gastric motility (GM) oscillation in the lower frequency range of photoplethysmography (PP G) signal. The objective of this article is to analyze the lower frequency oscillations of PPG signal using the novel autoregressive (AR) model and studying the presence of gastric motility (GM) rhythm in finger PPG. We acquired finger PPG and GM (measured using Electrogastrogram, EGG) signals at the sampling rate of 100 Hz simultaneously from 10 healthy subjects for 30 minutes duration in fasting and postprandial states. A lower frequency oscillation (� 0.05 Hz) called slow wave was extracted from PPG signal which looks similar to the slow wave of GM in both shape and frequency in the range (0 -0.1953) Hz using discrete wavelet transform (DWT). In a previous study, we demonstrated that there is a good cross-correlation exists between slow waves on both PPG and EGG signals [Annals of Biomedical Engg. DOl: 1O.1007/s10439-01O-0113-4]. Here in this study, power spectral density (P SD) of PPG and EGG slow waves were estimated using AR spectral estimation method. Dominant frequencies of PPG and EGG slow wave were estimated and the results imply that finger PPG signal reveals GM related information.