Assessment of slow wave propagation in multichannel electrogastrography by using noise-assisted multivariate empirical mode decomposition and cross-covariance analysis

Abstract Electrogastrography (EGG) is a noninvasive technique for recording the myoelectrical activity of the stomach. An electrogastrographic signal recorded by using a four-channel system with electrodes placed on the surface of the skin is a mixture of a low-frequency gastric pacesetter potential known as a slow wave, electrical activity from other organs, and random noise. The aim of this work was to investigate the possibility of detecting the propagation of the gastric slow wave from multichannel EGG data. Noise-assisted multivariate empirical mode decomposition (NA-MEMD) and cross-covariance analysis (CCA) are proposed as new detection tools. NA-MEMD was applied to attenuate the noise and extract the EGG signal from four channels, while CCA was performed to assess the time shift between the EGG signal channels. Validation of the method was performed using synthetic EGG signals and the methodology was tested on four young, healthy adults. After validation, the proposed method was applied for two kinds of human EGG data: 10-min (short) EGG data from the preprandial phase and 90–120-min (long) EGG data from the preprandial phase as well as the postprandial phase. The results obtained for both synthetic and human EGG data confirm that the proposed method could be a useful tool for assessing the propagation of slow waves. The time shift calculation from the preprandial phase of the EGG examination yielded more consistent results than the postprandial phase. The mean value of the slow wave time lag between neighbouring channels for synthetic data was found to be 4.99 ± 0.47  s. In addition, it was confirmed that the proposed method, that is, NA-MEMD and CCA together, are robust to noise.