Electrogastrogram Signals Research Articles

Filter banks and neural network-based feature extraction and automatic classification of electrogastrogram.

Dysrhythmia in gastric myoelectrical activity has been frequently observed in patients with gastric motor disorders and gastrointestinal symptoms. The assessment of the regularity of gastric myoelectrical activity is of great clinical significance. The aim of this study was to develop an automated assessment method for the regularity of gastric myoelectrical activity from the surface electrogastrogram (EGG). The method proposed in this paper was based on the filter bank and neural network. First, the EGG signal was divided into frequency subbands using filter bank analysis. Second, a parameter called the subband energy ratio (SER) was computed for each subband signal. A multilayer perceptron neural network was then used to automatically classify the EGG signal into four categories: bradygastria, normal, tachygastria, and arrhythmia, using the SER as the input. The EGG recording was made using the standard method of electrogastrography by placing electrodes on the abdominal surface. The study was performed in 40 patients with various gastric motor disorders, ten healthy adults, and ten healthy children. The neural network was trained and tested using the EGG data obtained from the patients. The regularity of gastric myoelectrical activity was assessed based on the classification of the minute-by-minute EGG segments. Using the running spectral analysis method as a gold standard, the proposed automated method had an accuracy of 100% for the training set and 97% for the test set. It was concluded that the proposed method provides an accurate and automatic assessment of the regularity of gastric myoelectrical activity from the EGG.

Blind separation of multichannel electrogastrograms using independent component analysis based on a neural network.

The electrogastrogram (EGG) is an abdominal surface measurement of gastric myo-electrical activity which regulates gastric contractions. It is of great clinical importance to record and analyse multichannel EGGs, which provide more information on the propagation and co-ordination of gastric contractions. EGGs are, however, contaminated by myo-electric interference from other organs and artefacts such as motion and respiration. The aim of the study is to separate the gastric signal from noisy multichannel EGGs without any information on the interference, using independent component analysis. A neural-network model is proposed, and corresponding unsupervised learning algorithms are developed to achieve the separation. The performance of the proposed method is investigated using artificial data simulating real EGG signals. Experimental EGG data are obtained from humans and dogs. The processed results of both simulated and real EGG data show the following: first, the proposed method is able to separate normal gastric slow waves from respiratory artefacts and random noises. It is also able to extract gastric slow waves, even when the EGG is contaminated by severe respiratory and ECG artefacts. Secondly, when the stomach contains various gastric electric signals with different frequencies, the proposed method is able to separate these different signals, as illustrated by simulations. These data suggest that the proposed method can be used to separate gastric slow waves, respiratory and motion artefacts, and intestinal myo-electric interference that are mixed in the EGG. It can also be used to detect gastric slow-wave uncoupling, during which the stomach has multiple gastric signals with different frequencies. It is believed that the proposed method may also be applicable to other biomedical signals.

Detection and deletion of motion artifacts in electrogastrogram using feature analysis and neural networks.

Electrogastrogram is a surface measurement of gastric myoelectrical activity, and electrogastrography has been an attractive method for physiological and pathophysiological studies of the stomach due to its noninvasive nature. Motion artifacts, however, ruin the electrogastrogram (EGG), and make the analysis very difficult and sometimes even impossible. They must be eliminated from EGG signals before analysis. Up to now, this can only be done by visual inspection, which is not only time-consuming but also subjective. In this study, a method using feature analysis and neural networks has been developed to realize automatic detection and elimination of the motion artifacts in EGG recordings by computer. Experiments were conducted to investigate the characteristics of different motion artifacts. Useful features were extracted, and different combinations of the features used as the input of the neural network were compared to obtain the optimal performance for the detection of motion artifacts using the artificial neural network.

What can be measured from surface electrogastrography. Computer simulations.

The aims of this study were to investigate the detectability of the propagation of the gastric slow wave from the cutaneous electrogastrogram (EGG) and the patterns of the EGG when the gastric slow waves are uncoupled. A mathematical model was established based on the volume conductor theory to simulate the transfer of the serosal gastric slow wave from the stomach to the abdominal surface. A number of computer simulations were conducted using the model, and the periodic cross-correlation function was used to estimate the phase shift between the four channels. It was found that the propagation of the gastric slow wave was detectable from the multichannel EGG signals. The detectability of the propagation was, however, associated with a number of factors, such as the thickness of the abdominal wall and the propagation velocity of the serosal slow wave. The amplitude of the EGG was found to be associated with the coupling/uncoupling and propagation velocity of the gastric slow wave. The amplitude of the EGG increased when the propagation velocity of the gastric slow wave increased. The amplitude of the EGG was substantially decreased when the gastric slow waves were uncoupled. The uncoupling of the gastric slow wave at a frequency of 3 cpm produced dysrhythmias in the EGG, including tachygastria, bradygastria, and arrhythmia. The power spectra of simulated different positional EGG signals showed similar patterns when the gastric slow wave was coupled and different and unpredictable patterns when the gastric slow wave was uncoupled. In conclusion, multichannel EGG recordings may be necessary to obtain more information on gastric slow waves from the abdominal electrodes. The propagation and coupling or uncoupling of the gastric slow wave may be detected from multichannel EGG recordings.

De-noising of electrogastrogram signals by wavelet shrinkage

De-noising of electrogastrogram signals by wavelet shrinkage

Relationship between surface electrogastrography and antropyloric pressures

The cutaneous electrogastrogram (EGG) and intraluminal antropyloroduodenal pressures were recorded in 12 healthy volunteers for 30-min periods during phase II of the interdigestive motor complex, during intraduodenal infusion of 10% triglyceride, and after intravenous erythromycin (3 mg/kg). During phase II, the frequency of the EGG was relatively constant in each individual, with a median frequency of 0.046 Hz [2.8 counts per minute (cpm)]. EGG frequency was greater (P < 0.05) than the median rate of antral pressure waves (1.8 cpm). The suppression of antral pressure waves (P < 0.05) and stimulation of isolated pyloric pressure waves (IPPWs) (P < 0.05) produced by triglyceride infusion were not associated with changes in EGG frequency compared with phase II. The frequency of the EGG and the rate of IPPWs were comparable. After erythromycin, EGG frequency was 0.03 Hz (1.8 cpm), less than during both phase II and triglyceride infusion (P < 0.05) and almost identical to the rate of antral pressure waves. Pressure waves were nearly always associated with an EGG signal. In contrast, the temporal relationship between the EGG signal and pressure waves was variable. During triglyceride infusion (r = 0.83, P < 0.001) and after erythromycin (r = 0.83, P < 0.001) there was a close (approximately 1:1) relationship between the rate of pressure waves and EGG frequency. However, there was no significant relationship (r = 0.32, not significant) between the number of pressure waves and EGG frequency frequency during pase II.(ABSTRACT TRUNCATED AT 250 WORDS)

Effect of barium meals on gastric electromechanical activity in man. A fluoroscopic-electrogastrographic study.

The relationship between the cutaneously recorded electrogastrogram (EGG) and gastric contractions in man is unclear. We investigated: (1) the relationship between the electrogastrogram (EGG) signals and gastric contractions elicited by barium meals and (2) the effects of barium meals on frequency and amplitude of EGG signals. As documented by fluoroscopy in four healthy subjects, barium meals stimulated three per minute gastric peristalsis which corresponded with simultaneously recorded three cycle per minute (cpm) EGG waves. Eighteen other healthy volunteers ingested 45% (w/v) or 60% barium suspensions. As determined by Fourier analysis, the dominant EGG frequency before barium was 3 cpm in 16 subjects; two subjects had no distinct frequency peaks. After barium ingestion, the mean amplitude or power at 3 cpm and 1 cpm increased, but the increase was significant only after 45% barium. (1) individual EGG waves after barium reflect gastric peristaltic sequences, which are reflected in increases in amplitude or power of 3 cpm EGG activity; (2) density or viscosity of the barium meal affects the gastric myoelectric response; and (3) mechanical correlates of 1 cpm EEG activity are unknown.

Electrogastrography: current issues in validation and methodology.

ABSTRACTIn this review we present some current applications of electrogastrography, relationships between the electrogastrogram (EGG) and electrical and contractile activity of the stomach, EGG recording technique, and the current status of electrogastrography. The authors conclude the following: (1) The 3 cpm frequency of the EGG reflects the frequency of pacesetter potentials occurring at the serosal and mucosal surfaces of the stomach. (2) An increase in the contractile activity of the corpnsantrum is associated with an increase in the amplitude of the 3 cpm EGG signal. (3) Tachygastria (5–9 cpm waves) recorded with the EGG reflects identical frequencies recorded with mucosal electrodes from the gastric antrum. (4) Low frequency components (approximately 1 cpm) appear in the EGG signal but their physiological significance is unknown at this time.