Analysis of gastric myoelectrical activity from the electrogastrogram signals based on wavelet transform and line length feature

Abstract

Electrogastrogram is used for the abdominal surface measurement of the gastric electrical activity of the human stomach. The electrogastrogram technique has significant value as a clinical tool because careful electrogastrogram signal recordings and analyses play a major role in determining the propagation and coordination of gastric myoelectric abnormalities. The aim of this article is to evaluate electrogastrogram features calculated by line length features based on the discrete wavelet transform method to differentiate healthy control subjects from patients with functional dyspepsia and diabetic gastroparesis. For this analysis, the discrete wavelet transform method was used to extract electrogastrogram signal characteristics. Next, line length features were calculated for each sub-signal, which reflect the waveform dimensionality variations and represent a measure of sensitivity to differences in signal amplitude and frequency. The analysis was carried out using a statistical analysis of variance test. The results obtained from the line length analysis of the electrogastrogram signal prove that there are significant differences among the functional dyspepsia, diabetic gastroparesis, and control groups. The electrogastrogram signals of the control subjects had a significantly higher line length than those of the functional dyspepsia and diabetic gastroparesis patients. In conclusion, this article provides new methods with increased accuracy obtained from electrogastrogram signal analysis. The electrogastrography is an effective and non-stationary method to differentiate diabetic gastroparesis and functional dyspepsia patients from the control group. The proposed method can be considered a key test and an essential computer-aided diagnostic tool for detecting gastric myoelectric abnormalities in diabetic gastroparesis and functional dyspepsia patients.

Electrogastrography in patients with gastric motility disorders

“…Several authors have worked with different wavelet analysis, for example, Barroso-Alvarado et al 18 used db4 wavelet analysis for the EGG signal, Nagendra et al 33 presented a summary of specific wavelet techniques, namely CWT, DWT, Stationary WT and Fractional WT. Many other studies applied ''db3'' for EGG signals to detect gastric arrhythmias, 9,11,22 Chaco´n et al 17 used fourth order Daubechies wavelet coefficients to evaluate signals from healthy and dyspepsia patients EGG, Sobral Cintra et al 32 showed that fitting a wavelet for EGG signals found with the standard ''db3'' to the fifth level wavelet provided the optimal parameterization values. In this study, ''db3'' was found to be the optimal wavelet for EGG signals at the fifth level and our results are partially consistent with other studies.…”

“…Moreover, DWT analyzes the signal in different frequency bands with different resolutions by decomposing the signal into coarse approximation and detail coefficients. 11,23 The equation for CWT (equation ( 1)): b: translation of the function over x(t), ''c'': wavelet function, *: the complex conjugation operation, ''1''/O(|''a''|): a normalization factor confirming the energy for all values of a. But the main problem of CWT, it is highly redundant and provides oversampling of the main waveform, so more coefficients are generated.…”

“…8,9 However, the major limitations of the EGG technique are accessibility, specialized data acquisition, signal processing, feature extraction, placement of surface electrodes on the abdominal skin, and visual inspection required to detect large artifacts. 10,11 Since signal processing of EGG data is considered essential for the analysis of the electrogastrogram, several traditional signal processing methods such as Fast Fourier Transform (FFT)-based spectral analysis and Short-Time Fourier Transform (STFT) have been developed with limited success. 12,13 Recently, the wavelet transform, which is based on functions that can be fully scaled and modulated, has gained acceptance among advanced signal processing techniques.…”

“…[14][15][16][17] Moreover, this approach has been used to detect noise and suppress artifacts associated with stimulation in EGG signals. 11,18 More recently, Deep Convolutional Neural Network Approach (CNN), Logistic Regression (LG), Support Vector Machine (SVM), and K Nearest Neighbor (KNN) Machine Learning (ML) algorithms have been successfully used to classify normal and abnormal slow gastric waves in the electrogastrogram with high accuracy. 19,20 In this study we successfully estimate the EGG signals accuracy using artificial neural networks (ANN) algorithms.…”

Frequency-specific electrogastrography as a non-invasive tool to measure gastrointestinal maturity in preterm infants

“…Because the EGG measurement is obtained from preterm babies longitudinally, the signal is expected to be highly non-stationary, and thus CWT is a more appropriate means used for time-frequency analysis. In the field of EGG [37][38][39][40][41][42][43][44] this is the first research report to utilize and implement CWT for determination of percentage time of non-stationary EGG rhythms spent in different gastric rhythm frequencies across three sub-feeding periods.…”