Electrogastrography Signal Research Articles

Data augmentation for generating synthetic electrogastrogram time series

To address an emerging need for large number of diverse datasets for rigor evaluation of signal processing techniques, we developed and evaluated a new method for generating synthetic electrogastrogram time series. We used electrogastrography (EGG) data from an open database to set model parameters and statistical tests to evaluate synthesized data. Additionally, we illustrated method customization for generating artificial EGG time series alterations caused by the simulator sickness. Proposed data augmentation method generates synthetic EGG data with specified duration, sampling frequency, recording state (postprandial or fasting state), overall noise and breathing artifact injection, and pauses in the gastric rhythm (arrhythmia occurrence) with statistically significant difference between postprandial and fasting states in > 70% cases while not accounting for individual differences. Features obtained from the synthetic EGG signal resembling simulator sickness occurrence displayed expected trends. The code for generation of synthetic EGG time series is not only freely available and can be further customized to assess signal processing algorithms but also may be used to increase data diversity for training artificial intelligence (AI) algorithms. The proposed approach is customized for EGG data synthesis but can be easily utilized for other biosignals with similar nature such as electroencephalogram.Graphical abstract

Reliability and validity of brain-gastric phase synchronization.

Recent studies have reported that various brain regions, mainly sensory, unimodal regions, display phase synchronization with the stomach's slow (0.05 Hz) myoelectrical rhythm. These gastric-brain interactions have broad implications, from feeding behavior to functional gastrointestinal disorders. However, in contrast to other interoceptive signals (e.g., heart rate) and their relation to the brain, little is known about the reliability of these gastric-brain interactions, their robustness to artifacts such as motion, and whether they can be generalized to new samples. Here we examined these aspects in 43 subjects that had undergone multiple runs of concurrent electrogastrography (EGG), brain fMRI, and pulse oximetry. We also repeated all analyses in an open dataset of a highly sampled individual. We found a set of brain regions that were coupled with the EGG signal after controlling for non-grey matter (GM) signals, head motion, and cardiac artifacts. These regions exhibited significant overlap with previous work. However, we also showed that prior to confound regression, the spatial extent of the gastric network was largely overestimated. Finally, we found substantial test-retest reliability in both the brain and the gastric signals when estimated alone, but not for measures of gastric-brain synchrony. Together, these results provide methodological scaffolding for future research into brain-stomach interactions and for a better understanding of the role of the gastric network.

Gastric Disorder Analysis Using Hybrid Optimization with Machine Learning

The stomach and all of its appendages, which include the oesophagus, duodenum, small intestine, and large intestine, amongst others, all play a crucial function within this system. Stomach dysrhythmias, which are linked to problems with the movement of gastrointestinal contents, affect a significant number of individuals all over the globe. These problems include inappropriate digestion (dyspepsia), nausea (vomiting sensation) for no apparent reason, vomiting, abdominal pain, stomach ulcers, gastroesophageal reflux disease, and other disorders. During the process of finding the anomalies, it is possible that a number of techniques, including as imaging, endoscopy, electrogastrogram, and clinical analysis, will be used. Electrogastrography signals, also known as electrogastrograms (EGG), were captured using surface Ag/AgCl electrodes that were put over the stomach in 20 healthy persons before the data was gathered and pre-processed. The datasets were produced from these signals (8 Females and 12 Males). In addition to this, the datasets were obtained from 10 individuals who were suffering from various stomach illnesses (3 Females and 8 Males). In the stage known as “pre-processing,” which needs the obtained dataset to be treated in advance, any noise that was present in the signal is removed. In order to rid the data of any noise and increase the overall quality of the input data, a technique that is known as the Wiener filter is used. A technique known as Hybrid Grey Wolf Optimization with Particle Swarm Optimization is utilized in the process of feature selection. This algorithm is responsible for removing any extraneous data from the features that have been collected from the signal. The procedure is sped up as a result of this. The classifiers get the qualities that have been chosen as their input in order to carry out an analysis of the many stomach disorders, such as primary gastric lymphoma, gastrointestinal stromal tumour (GIST), and neuroendocrine tumor. This enables the classifiers to do the analysis (carcinoid). The Multi-class Feed Forward Neural Network Classifier (MCFFN) is used to carry out the classification process. This classifier provides the stages along with the classes. The accuracy, sensitivity, and specificity of the classification process are taken into account in the calculation of performance measures.

Effect of Olfactory Stimulation with Vanilla Odor on Degree of Gastric Myoelectrical Activity

Olfactory stimulation elicits various physiological responses. However, few reports exist on the changes in gastric motility during olfactory stimulation in humans. In this regard, we carried out electrogastrography (EGG) to non-invasively measure the gastric myoelectrical activity, which regulates gastric motility. Moreover, subjective sensory evaluation was performed to determine which characteristics of vanilla odor at two different concentrations affect the myoelectrical activity. The participants consisted of eight healthy young males. EGG and electrocardiography (ECG) recordings were obtained approximately 20 min prior to and during olfactory stimulation. Autonomic nervous system activity was evaluated in terms of heart rate variability (HRV) and mean heart rate (HR) from ECG signals. EGG signals were analyzed by spectral analysis. In addition, the translation error was estimated by the Wayland algorithm. Sensory evaluation was performed using the Visual Analog Scale (VAS). There were no significant differences in HRV and HR values and results of spectral analyses of EGG signals in all sample presentations. The translation error of EGG signals and the rating of perceived odor intensity significantly increased in a concentration-dependent manner. There was a strong positive correlation between translation error and odor intensity. The correlation found between translation error and odor intensity suggests that the higher the vanilla odor intensity was perceived, the greater the randomness of EGG signals was. Our results suggest that the application of the Wayland algorithm to EGG signals can be used as an objective indicator in odor evaluation.

Electrogastrography in Autonomous Vehicles-An Objective Method for Assessment of Motion Sickness in Simulated Driving Environments.

Autonomous vehicles are expected to take complete control of the driving process, enabling the former drivers to act as passengers only. This could lead to increased sickness as they can be engaged in tasks other than driving. Adopting different sickness mitigation techniques gives us unique types of motion sickness in autonomous vehicles to be studied. In this paper, we report on a study where we explored the possibilities of assessing motion sickness with electrogastrography (EGG), a non-invasive method used to measure the myoelectric activity of the stomach, and its potential usage in autonomous vehicles (AVs). The study was conducted in a high-fidelity driving simulator with a virtual reality (VR) headset. There separate EGG measurements were performed: before, during and after the driving AV simulation video in VR. During the driving, the participants encountered two driving environments: a straight and less dynamic highway road and a highly dynamic and curvy countryside road. The EGG signal was recorded with a proprietary 3-channel recording device and Ag/AgCl cutaneous electrodes. In addition, participants were asked to signalize whenever they felt uncomfortable and nauseated by pressing a special button. After the drive they completed also the Simulator Sickness Questionnaire (SSQ) and reported on their overall subjective perception of sickness symptoms. The EGG results showed a significant increase of the dominant frequency (DF) and the percentage of the high power spectrum density (FSD) as well as a significant decrease of the power spectrum density Crest factor (CF) during the AV simulation. The vast majority of participants reported nausea during more dynamic conditions, accompanied by an increase in the amplitude and the RMS value of EGG. Reported nausea occurred simultaneously with the increase in EGG amplitude. Based on the results, we conclude that EGG could be used for assessment of motion sickness in autonomous vehicles. DF, CF and FSD can be used as overall sickness indicators, while the relative increase in amplitude of EGG signal and duration of that increase can be used as short-term sickness indicators where the driving environment may affect the driver.

Gastric slow wave rhythm identification using new approach based on noise-assisted multivariate empirical mode decomposition and Hilbert-Huang transform.

Electrogastrography (EGG) is the method of cutaneous recording of the myoelectrical activity of the stomach. A multi-channel signal is recorded non-invasively by means of electrodes placed outside the epigastric area. The normal electrical rhythm of the stomach (slow wave) may become significantly disturbed due to disorders of gastrointestinal tract. Abnormally fast electrical rhythms are termed tachygastria, while abnormally slow rhythms are known as bradygastria. Because some features of biological signals may go undetected using the classical methods of signal spectral analysis, we propose a new method for EGG rhythm identification. In this study, the calculation of the basic rhythms of multi-channel EGG signals is performed by means of the noise-assisted multivariate empirical mode decomposition (NA-MEMD) and Hilbert-Huang transform (HHT), using EGG data from eight healthy subjects. The results were compared with those obtained using classical spectral analysis. The mean values of the normogastric index for preprandial and two postprandial stages were found to be 64.78±11.37%, 61.29±15.86%, and 63.80±13.24%, respectively. The obtained values of normogastric index are consistent with the normal human physiological value, which is approximately 70% for healthy subjects. This method is able to capture features of the signal which are mostly undetectable by standard EGG processing methods. The EGG dominant rhythm identification using the instantaneous normogastric, bradygastric, and tachygastric indices provides new insights into biological EGG patterns.

Lessons Learned: Gastric Motility Assessment During Driving Simulation.

In the era of technological advances and innovations in transportation technologies, application of driving simulators for the investigation and assessment of the driving process provides a safe and suitable testing environment. Although driving simulators are crucial for further improvements in transportation, it is important to resolve one of their main disadvantages–simulator sickness. Therefore, suitable methods for the assessment of simulator sickness are required. The main aim of this paper was to present a non-invasive method for assessing simulator sickness by recording gastric myoelectrical activity–electrogastrography. Open-source hardware for electrogastrography together with recordings obtained in 13 healthy volunteers is presented, and the main aspects of signal processing for artifact cancellation and feature extraction were discussed. Based on the obtained results, it was concluded that slow-wave electrical gastric activity can be recorded during driving simulation by following adequate recommendations and that proposed features could be beneficial in describing non-ordinary electrogastrography signals.

EGG DWPack: System for Multi-Channel Electrogastrographic Signals Recording and Analysis

Electrogastrography (EGG) is a non-invasive examination method for investigating the myolectrical activity of a stomach. Nowadays, abdominal surface electrogastrography is the one of methods of stomach examination that is used for diagnosing patients with chronic intractable nausea, vomiting and gastroparesis. The electrogastrographic signals are recorded by using cutaneous electrodes placed on the stomach surface. EGG DWPack system is a highly developed and easy to use software package for four channel electrogastrography recording and analysis. The part of the software for analysis is a MATLAB based software and requires the specific ASCII format of the EGG data. The analyzed EGG signals could be conditioned with the wide range of sampling frequency and various resolutions of analog to digital conversion. Additionally, if the EGG data fulfills certain conditions associated with sampling frequency, the software can be used to study the basic parameters of heart rate variability (HRV) simultaneously with the EGG parameters. The software includes different digital filters for the EGG signal extraction and tools for artifacts exclusion. The software computes the majority of EGG parameters which are commonly used in a clinical practice. The EGG analysis can be made for several adjustable analysis settings and various methods, and it can optimize the analysis methods for different preferences or requirements. The analysis result can be saved in a MAT-file, and exported to MS Excel and ASCII files. Validation of the software was performed using synthetic and real EGG signals. This paper contains, as an example of use, an analysis of four synthetic, and fourteen human 4-channel EGG data recording with water, yogurt and a solid meal stimulation. The mean values of the dominant frequency for fast, and postprandial stage were found to be 2.96±0.21 cpm (cycle per minute), and 3.05±0.33 cpm, respectively. The values established in the validation process are consistent with typical human physiological values. In addition, the results were compared to outcomes from commercial system. The results of validation have proved that EGG DWPack software produces reliable outcomes. The software is available for free of charge for Windows operating system for the all possible non commercial use.

A new approach for denoising multichannel electrogastrographic signals

Abstract Electrogastrography (EGG) can be considered as a non-invasive method for the measurement of gastric myoelectrical activity. The multichannel signal is non-invasively captured by disposable electrodes placed on the surface of a stomach. The recorded signal can include not only EGG components, but also the interfering signals from other organs, for instance, the disturbances connected with respiratory movements and random noise. In order to correctly calculate the parameters of the EGG examination and improve the patient's diagnosis, the EGG signal requires effective methods for removing disturbances. The aim of this work was to investigate a new approach for denosing the multichannel electrogastrographic signals, performed by means of the Noise-Assisted Empirical Mode Decomposition (NA-MEMD) and adaptive filtering. The proposed method uses NA-MEMD for extracting the reference signal for adaptive filtering in the cosine domain. The suggested technique was validated by comparing the obtained results with the outcomes acquired by the reference method based on the classical bandpass filtering, Independent Component Analysis (ICA) and adaptive filtering. The effectiveness of the proposed algorithm was established by examining the influence of adaptive filtering on the basic diagnostic parameters, calculated from the EGG signal, such as the dominant frequency (DF), the normogastric rhythm index (NI), the frequency instability coefficient (FIC), and the power instability coefficient (PIC). In addition, the effectiveness of the noise attenuation by the proposed method was verified. The paper presents the results of research carried out for the five healthy subjects. Validation of the proposed method was performed using real human EGG signals and real EGG signals with added synthetic noise.

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

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.

Electrogastrography in Delayed Gastric Emptying

Disorders of gastric motility are generally manifested by an abnormal rate of gastric emptying. The emptying process of the stomach is very complex, and knowledge is limited to the observation that gastric emptying rate is a highly variable phenomenon, and that delayed gastric emptying is frequently the case. The advances in the knowledge of the physiology of gastric muscle and enteric nerves, and the recognition of the patterns of organization of smooth muscle contractions gave a new input to the study of gastric motility. The gastric emptying can be monitored in various ways, such as manometry, scintigraphy, or electrogastrography (EGG). Recently, EGG has received more attention. There is correlation between the EGG signal obtained from body surface electrodes and signals obtained directly from electrodes locates in the gastric muscle (serosal records). Some studies showed an association between EGG-findings and gastric motility disorders, and indicate that EGG is a reliable, non-invasive, useful method to detect gastric myoelectric activity.

The Use of Continuous Wavelet Transform Based on the Fast Fourier Transform in the Analysis of Multi-channel Electrogastrography Recordings.

This paper presents the analysis of multi-channel electrogastrographic (EGG) signals using the continuous wavelet transform based on the fast Fourier transform (CWTFT). The EGG analysis was based on the determination of the several signal parameters such as dominant frequency (DF), dominant power (DP) and index of normogastria (NI). The use of continuous wavelet transform (CWT) allows for better visible localization of the frequency components in the analyzed signals, than commonly used short-time Fourier transform (STFT). Such an analysis is possible by means of a variable width window, which corresponds to the scale time of observation (analysis). Wavelet analysis allows using long time windows when we need more precise low-frequency information, and shorter when we need high frequency information. Since the classic CWT transform requires considerable computing power and time, especially while applying it to the analysis of long signals, the authors used the CWT analysis based on the fast Fourier transform (FFT). The CWT was obtained using properties of the circular convolution to improve the speed of calculation. This method allows to obtain results for relatively long records of EGG in a fairly short time, much faster than using the classical methods based on running spectrum analysis (RSA). In this study authors indicate the possibility of a parametric analysis of EGG signals using continuous wavelet transform which is the completely new solution. The results obtained with the described method are shown in the example of an analysis of four-channel EGG recordings, performed for a non-caloric meal.

The extraction of the new components from electrogastrogram (EGG), using both adaptive filtering and electrocardiographic (ECG) derived respiration signal.

Electrogastrographic examination (EGG) is a noninvasive method for an investigation of a stomach slow wave propagation. The typical range of frequency for EGG signal is from 0.015 to 0.15 Hz or (0.015–0.3 Hz) and the signal usually is captured with sampling frequency not exceeding 4 Hz. In this paper a new approach of method for recording the EGG signals with high sampling frequency (200 Hz) is proposed. High sampling frequency allows collection of signal, which includes not only EGG component but also signal from other organs of the digestive system such as the duodenum, colon as well as signal connected with respiratory movements and finally electrocardiographic signal (ECG). The presented method allows improve the quality of analysis of EGG signals by better suppress respiratory disturbance and extract new components from high sampling electrogastrographic signals (HSEGG) obtained from abdomen surface. The source of the required new signal components can be inner organs such as the duodenum and colon. One of the main problems that appear during analysis the EGG signals and extracting signal components from inner organs is how to suppress the respiratory components. In this work an adaptive filtering method that requires a reference signal is proposed. In the present research, the respiratory component is obtained from non standard ECG (NSECG) signal. For purposes of this paper non standard ECG (namely NSECG) is used, because ECG signal was recorded by other than the standard electrodes placement on the surface of the abdomen. The electrocardiographic derived respiration signal (EDR) is extracted using the phenomena of QRS complexes amplitude modulation by respiratory movements. The main idea of extracting the EDR signal from electrocardiographic signal is to obtain the modulating signal. Adaptive filtering is done in the discrete cosine transform domain. Next the resampled HSEGG signal with attenuated respiratory components is low pass filtered and as a result the extended electrogastrographic signals, included EGG signal and components from other inner organs of digestive system is obtained. One of additional features of the proposed method is a possibility to obtain simultaneously recorded signals, such as: non-standard derivation of ECG, heart rate variability signal, respiratory signal, and EGG signal that allow investigating mutual interferences among internal human systems.

Prebiotics Improve Gastric Motility and Gastric Electrical Activity in Preterm Newborns

The aim of this double-blind, randomized, placebo-controlled study was to evaluate the effect of a prebiotic mixture on gastric motility in preterm newborns. After a feeding period of 15 days, gastric electrical activity was measured by electrogastrography, and the gastric emptying time was studied by ultrasound technique. No difference was seen in the daily increase of body weight, and no adverse events have been reported. The percentage of time in which propagation was detected in the electrogastrography signal was twice in newborns receiving formula with prebiotics with respect to placebo, and the gastric half-emptying time was 30% faster in the prebiotic group than the placebo group. Prebiotic oligosaccharides can modulate the electrical activity and the gastric emptying and may improve the intestinal tolerance of enteral feeding in preterm infants.

Trans-cutaneous electrogastrographic study of gastric myoelectric activity in transposed intrathoracic stomach after esophagectomy

We examined the feasibility of trans-cutaneous electrogastrography (EGG) in recording myoelectric activity of the transposed thoracic stomach after esophagectomy. Nineteen patients who had Ivor-Lewis esophagectomy were studied. The EGG signal was recorded using cutaneous electrodes placed over the lower sternum. Eleven patients who underwent total gastrectomy served as controls. Normal rhythm pattern (2.4-3.6 cpm > or = 70%) and power ratio (PR > or = 2) was observed in five and 12 patients, respectively, after esophagectomy. The observation of normal gastric rhythm was more frequent in the postprandial period in the esophagectomy group (median 42.6%vs. 7.4%, P = 0.01), and the PR was significantly higher (median 2.27 vs. 1.38, P = 0.013) than the gastrectomy group. Feeding further increased the prevalence of normal gastric slow wave in the esophagectomy group (median 14.8% to 42.6%, P = 0.002) and improved the stability of dominant frequency (median 78% to 67%, P = 0.015). We conclude that gastric myoelectric activities of thoracic transposed stomach can be detected from cutaneous sternal electrodes. This represented a preservation of gastric motility even when the stomach is pulled up to the thorax as a substitute for the esophagus.

Centre-specific multichannel electrogastrographic testing utilizing wavelet-based decomposition

Although the principles of electrogastrography (EGG) have been known for years, the clinical utility of EGG has not been clearly demonstrated, and EGG recording and analysis techniques have not been fully standardized. The aim of this study was to develop a multichannel EGG technique for detecting abnormal gastric motility using an EGG database specifically designed for a particular testing centre, maximizing the sensitivity and the specificity of the test. Eight healthy volunteers formed a reference group to which 4 study groups (17 gastro-oesophageal reflux disease (GORD) patients, 7 functional dyspepsia patients, 8 post-fundoplication patients and 12 healthy volunteers) were compared. Eight-channel EGG was recorded in the postprandial and fasting states for 30 min each. The recorded signals were wavelet compressed and the resulting error (per cent root mean square difference (PRD)) after the compression was utilized to compare the study groups to the reference group. A threshold in the number of channels with significantly different PRD values was introduced. Sensitivity (SE), specificity (SP) and correct classification rate (CC) of the test in recognizing each clinical condition in the study groups for several channel thresholds and compressions were calculated, and were maximized. Increasing the compression and channel threshold levels improved the specificity, but decreased the sensitivity of the multichannel EGG test. An optimal combination region was identified based on a centre-specific adjustment of the channel threshold and the wavelet compression. The achieved maximum sensitivity, specificity and correct classification for this region in our test centre were as follows: GORD—SE 82.4%, SP 83.3%, CC 82.8%; functional dyspepsia—SE 100%, SP 75%, CC 84.2%; post-fundoplication—SE 75.0%, SP 83.3%, CC 80.0%. The utilization of a wavelet-based decomposition technique to process multichannel EGG signals can be a very effective method for enhancing the clinical utility of EGG, provided it is specifically developed for a given testing centre.

Estimation of wavelet and short-time Fourier transform sonograms of normal and diabetic subjects’ electrogastrogram

Electrogastrography (EGG) is a noninvasive way to record gastric electrical activity of stomach muscle by placing electrodes on the abdominal skin. Our goal was to investigate the frequency of abnormalities of the EGG in real clinical diabetic gastroparesis patients using WT method and to compare performance of STFT and WT methods in the case of time–frequency resolution. The results showed that WT sonograms can be used to classify patients successfully as healthy or sick. And also, due to the fact that the WT method does not suffer from some intrinsic problems that affect the STFT method, one can see that the WT method can help improve the quality of the sonogram of the EGG signals.

354 Surface Electrogastrography in Growing Preterm Neonates

Background: The ability to provide enteral nutrition to preterm infants is limited by immaturity of GI motor function. Manometry and electrogastrography(EGG) have been used to study gastric dysrhythmias and GI motor disorders in adults. EGG has been shown to be a reliable non-invasive alternative technique to gastric manometry in preterm infants. The information on pattern and maturation of EGG is limited in preterm infants. Objective: To investigate the effect of gestational and postnatal age on the pattern of gastric myoelectrical activity in preterm infants without feeding intolerance and those with gastroesophageal reflux(GER). Methods: EGG signals were recorded at 10Hz and bandpass filtered between 1.2 and 18 cycles/min(cpm), 30 minutes before and 2 hours after feeds. Signals were analyzed by FFT spectral analysis. The percentage of regular 2–4 cpm normal slow wave activity (NSW) and tachygastria(4–10cpm)was determined from a running power spectrum analysis generated at 10 minute intervals. Results: 70 EGGs were obtained in 31 infants; BW 1550±758SD g, range 618 to 3494, GA 30.7±4.5 SD wks, range 25 to 40, study age 38±24SD days, range 2–112. Preterm infants at term PCA had lower pre feeds NSW than those born at term(30.1±5.9% vs 41.7±14.8%). After feeds NSW increased in both groups ( 37.9±3.8% vs 52.1±10.8%). GER babies had 35% lower NSW than controls before feeds. NSW increased 53% after feeds in GER babies vs 13% in controls. tachygastria activity was 1.4 vs 7.8% (p=0.03) pre feeds, and 6.1 vs 6.8% post feeds in GER group as compared to controls. Conclusions: Preterm infants have less normal slow wave activity as compared to term infants which persisted up to term PCA. The response to feeds was less but similar to babies born at term. Maturation of Gastric myoelectrical activity of preterm infants with GER appears to lag behind that of infants without feeding intolerance.

NEW ONSET TYPE 1 DIABETES MELLITUS (T1DM) IN CHILDREN-PREVALENCE OF SILENT GASTROPATHY ESTABLISHED BY MEANS OF ELECTROGASTROGRAPHY AND ITS CORRELATION WITH METABOLIC CONTROL

Many studies have aimed at validating the use of electrogastrography (EGG) measurements as a noninvasive indicator of gastropathy and high prevalence of gastric myoelectrical dysrhythmias in diabetic subjects has been reported. Although little is known as regards the GI status in the early stage of diabetes, there is no available data concerning gastric myoelectrical activity in children with newly diagnosed T1DM. The aim: Was to evaluate the gastric myoelectrical activity in children with new onset (<1 year) T1DM in respect of metabolic control. Material and Methods: 42 children with T1DM duration of <1 year, aged 5-18 yrs (mean 12,87 ± 3,09 yrs) and 35 healthy controls were enrolled. In all subjects precutaneous EGG (PC Polygraff Synectics Medical Inc.) was performed. Fasting (30 min) and postprandial (60 min) periods were analyzed in terms of: the percentage of normogastria (2,4-3.6 cpm), bradygastria (<2.4 cpm), tachygastria (3,7-10 cpm), dominant frequency instability coefficient DFIC and power ratio PR. In subjects with T1DM hemoglobin A1c (HbA1c) (HPLC method) and blood glucose levels were measured (GLUKOTREND BOEHRINGER Mannheim). The evaluation of ANS was based on deep breath test and heart rate variability monitoring (HRV). The study protocol was approved by the Medical University Ethics Committee. Results: In all subjects, but one (2,38%), the screening for neuropathy was negative. In 28,57% of T1DM and 91.42% controls (p < 0.001) normal EGG criteria were fulfilled. The fasting and postprandial percentage of gastric dysrhythmias were significantly higher in T1DM subjects than in controls (p < 0,001). The significant difference between T1DM and controls was observed in he percentage of fasting bradygastria (53,13 ± 32,74% vs. 20 ± 17,81%; p < 0.01) and normal pre-prandial rhythm (36,97 ± 30,65% vs. 71,8 ± 13,74%; p < 0,01). In the postprandial period in T1DM normogastria was present in 50% subjects vs. 28,57% preprandially (p < 0,001), mean normogastria percentage increased (71,79 ± 22,89% vs. ±36,97 ± 30,65) and that of bradygastria decreased (22,04±20,44% vs. 53,13 ± 32,74%); the EGG signal proved to be more stabile (DFIC 57,25 ± 35,74 vs. 31,93 ± 22,11; p < 0,05). PR was comparable in T!DM and in controls (2,01 ± 2,33 vs 2,54 ± 1,18; NS). The percentage of fasting bradygastria and normogastria were correlated with glycemia (r = −0,55 and r = 0,51; p < 0,05), and postprandial DF with postprandial glycemia (r = 0,41; p < 0,05). There was no correlation between HbA1c and EGG. Conclusions: Our results proved that the derangement of the gastric myoelectrical activity is present in a high proportion of children with early stage of type 1 diabetes without neuropathy Glucose level influences gastric myoelectrical activity, whereas long-term metabolic control (HbA1c) does not correlate with EGG exponents in new onset T1DM children.

Electrogastrographic biofeedback: a technique for enhancing normal gastric activity

Biofeedback has been used effectively for approximately 40 years as a technique to help individuals learn to control muscle activity, brain waves, certain autonomic nervous system responses such as heart rate and blood pressure, and, more recently, constipation and fecal and urinary incontinence. In the present study, biofeedback of electrogastrographic (EGG) activity was used to determine the extent to which healthy subjects could increase normal 3 cpm gastric myoelectric activity. Thirteen experimental participants were provided with biofeedback of their EGG activity on a computer screen, and instructed to try to make their own EGG signal match a simulated 3 cpm signal that was also present on the screen. Subjects were told to relax using various forms of imagery, and to heighten their awareness of their stomach activity. Thirteen control participants did not receive any biofeedback but were otherwise given the same instructions. Both groups showed an increase in 3 cpm activity while relaxing. However, over four trials separated by 2-7 days, experimental participants showed an increased percentage of 3 cpm activity during biofeedback sessions, while control participants showed a decrease in 3 cpm activity during the corresponding sessions. In conclusion, we have demonstrated that healthy subjects can increase their normal gastric 3 cpm activity with the use of EGG biofeedback.