Electrogastrogram Signals Research Articles

Biosensor-Assisted Method for Abdominal Syndrome Classification Using Machine Learning Algorithm.

The digestive system is one of the essential systems in human physiology where the stomach has a significant part to play with its accessories like the esophagus, duodenum, small intestines, and large intestinal tract. Many individuals across the globe suffer from gastric dysrhythmia in combination with dyspepsia (improper digestion), unexplained nausea (feeling), vomiting, abdominal discomfort, ulcer of the stomach, and gastroesophageal reflux illnesses. Some of the techniques used to identify anomalies include clinical analysis, endoscopy, electrogastrogram, and imaging. Electrogastrogram is the registration of electrical impulses that pass through the stomach muscles and regulate the contraction of the muscle. The electrode senses the electrical impulses from the stomach muscles, and the electrogastrogram is recorded. A computer analyzes the captured electrogastrogram (EGG) signals. The usual electric rhythm produces an enhanced current in the typical stomach muscle after a meal. Postmeal electrical rhythm is abnormal in those with stomach muscles or nerve anomalies. This study considers EGG of ordinary individuals, bradycardia, dyspepsia, nausea, tachycardia, ulcer, and vomiting for analysis. Data are collected in collaboration with the doctor for preprandial and postprandial conditions for people with diseases and everyday individuals. In CWT with a genetic algorithm, db4 is utilized to obtain an EGG signal wave pattern in a 3D plot using MATLAB. The figure shows that the existence of the peak reflects the EGG signal cycle. The number of present peaks categorizes EGG. Adaptive Resonance Classifier Network (ARCN) is utilized to identify EGG signals as normal or abnormal subjects, depending on the parameter of alertness (μ). This study may be used as a medical tool to diagnose digestive system problems before proposing invasive treatments. Accuracy of the proposed work comes up with 95.45%, and sensitivity and specificity range is added as 92.45% and 87.12%.

Compression Performance Analysis of Electrogastrogram (Egg) Using Different Wavelet Transforms For Telemedicine

Electrogastrogram (EGG) is the non-invasive graphical representation of stomach’s electrical activity for diagnosing stomach Disorders. EGG signal compression has an important role in Tele-diagnosis, Tele-prognosis and survival analysis of all stomach dysrhythmias, when the patient is geographically isolated. There are plenty of signal compression techniques available and proposed over years. Due to some drawbacks like high cost, signal loss and poor compression ratio leads the signal into inefficient at receiver’s end. The compression of digital EGG in telemedicine holds three major advantages like efficient & economic usage of storage data, reduction of the data transmission rate and good transmission bandwidth conversation. In this study EGG signals are tested with different wavelet transforms such as Biorthogonal, coiflet, Daubechies, Haar, reverse biorthogonal and symlet wavelet transforms using MATLAB software, in order to find best performance wavelet for telemedicine. The performance is mathematically analyzed using the values of Percent Root Mean Square Difference (PRD), Compression ratio (CR) and recovery ratio. The result of better compression performance in signal compression could definitely be a great asset in telemedicine field for transferring more quantities of Biological signals.

Diagnosis of Type 2 Diabetes Using Electrogastrograms: Extraction and Genetic Algorithm–Based Selection of Informative Features

Background Electrogastrography is a noninvasive electrophysiological procedure used to measure gastric myoelectrical activity. EGG methods have been used to investigate the mechanisms of the human digestive system and as a clinical tool. Abnormalities in gastric myoelectrical activity have been observed in subjects with diabetes. Objective The objective of this study was to use the electrogastrograms (EGGs) from healthy individuals and subjects with diabetes to identify potentially informative features for the diagnosis of diabetes using EGG signals. Methods A total of 30 features were extracted from the EGGs of 30 healthy individuals and 30 subjects with diabetes. Of these, 20 potentially informative features were selected using a genetic algorithm–based feature selection process. The selected features were analyzed for further classification of EGG signals from healthy individuals and subjects with diabetes. Results This study demonstrates that there are distinct variations between the EGG signals recorded from healthy individuals and those from subjects with diabetes. Furthermore, the study reveals that the features Maragos fractal dimension and Hausdorff box-counting fractal dimension have a high degree of correlation with the mobility of EGGs from healthy individuals and subjects with diabetes. Conclusions Based on the analysis on the extracted features, the selected features are suitable for the design of automated classification systems to identify healthy individuals and subjects with diabetes.

The High-Dimensional Signal Classification of Electrogastrogram for Detection of Gastric Motility Disorders

The electrogastrogram (EGG) can detect the gastric electromyogram activity, and then reflect the relative change of the rhythm as well as amplitude of the slow wave of the electromyogram. As EGG has the advantages of convenient, painless, non-invasive and accurate measurement of gastric electromyogram activity, it can not only be used to evaluate the effects of gastromotor drugs and gastrointestinal hormones, but also to distinguish healthy people from functional dyspepsia, patients with gastric cancer and patients with low gastric motility according to the results of parameter analysis in EGG. This paper proposes an EGG signal processing and classification method to realize the potential role of EGG in the diagnosis and management of gastrointestinal diseases. First, EGG signal collection was conducted on normal people and patients, and then the test signal was described as accurately as possible according to some key features of the gastric waveform. Based on the collected data, we developed an indicator that can classify high-dimensional signals and provide an indicator that can distinguish or identify two kinds of signal-related indicators. In this way, EGG signals are associated with specific conditions for clinical diagnosis of gastrointestinal dysrhythmia and even for efficacy evaluation.

SPARC: Brain‐stomach Synchrony Observed with Functional Magnetic Resonance Imaging and Electrogastrogram in Rats

PurposeThe gut communicates with the brain, allowing the gastrointestinal state to influence cognition and emotion and vice versa. In the resting state, gastric electrical activity has been shown to be synchronized with the blood‐oxygen‐level‐dependent (BOLD) signal in the so‐called gastric network in humans [1]. However, the finding has been rarely replicated. Here, we explored the gut‐brain synchrony in rats. Whole‐brain functional magnetic resonance imaging (fMRI) was acquired simultaneously with electrogastrogram (EGG) recording. Cross‐correlation between EGG and BOLD signals was used to map resting state networks influenced by gastric activity.MethodBrain fMRI was performed on three SD rats together with multi‐channel EGG recording. Each rat was trained to consume diet gel enriched with Gadolinium contrast media. Before the experiment, the rat was fed with 5g diet gel. After the feeding, the animal was anesthetized with continuous dexmedetomidine and isoflurane. The multi‐channel EGG signal was recorded using an electrophysiological recording system (Tucker Davis Technologies Inc.) together with fMRI scans using a 7‐tesla small‐animal MRI system (BioSpec 70/30, Bruker). The principal component analysis was applied to EGG signals. The correlation between the voxel‐wise fMRI signal and the component‐wise EGG signal was calculated, yielding multiple gut‐associated brain networks. The voxel‐wise fMRI signal was also modeled as a linear combination of EGG component time series with varying time shifts.ResultsFMRI was acquired simultaneously with the EGG recording ( Fig. 1A). EGG was denoised and cross‐correlated with the BOLD activity in every voxel to map an EGG‐correlated network (or the gastric network) ( Fig. 1C). The BOLD signal was averaged across voxels in the gastric network. The resulting network‐wide signal was found to be phase‐locked to EGG with a time delay ( Fig. 1B). Accounting for the delay variable across voxels, we mapped an even broader network phase‐locked with EGG ( Fig. 2A). Different EGG‐coupled regions were found to lag behind EGG by different times, ranging from 4 to 12 seconds ( Fig. 2B & C). From the EGG‐coupled BOLD response, the visual and auditory cortex had a relatively longer delay, whereas the somatosensory and anterior cingulate cortex had a shorter delay. The cingulate cortex showed a complex response pattern, likely implying multiple functional roles with respect to the gut.ConclusionIn rats, brain activity is intrinsically synchronized with gastric activity at a specific resting state network. In this network, different brain regions are time‐locked to gastric activity with varying time delays. Speculatively, the pattern of time delay may reflect how gastric information propagates through a cascade of brain regions for sequential processing.Support or Funding InformationThis study was funded by National Institutes of Health’s SPARC ‐ Stimulating Peripheral Activity to Relieve Conditions ‐ program (OT2OD023847).Simultaneous EGG and brain fMRI recording. (A) shows the layout of the 32‐channel electrode array for EGG recording. (B) are examples of EGG and fMRI time series. (C) shows an example of the EGG correlation map. The correlation between EGG and fMRI time series is color‐coded.Figure 1The EGG network considering the phase delays between EGG and fMRI signal. (A) is a summary of the EGG network. The color represents the F statistic of the multiple linear regression. (B) selects six voxels in the EGG network. The curve shows the relation between the F statistic and EGG‐fMRI delays for the selected voxels, which are marked with blue box in (A). (C) is the phase delay map that delineates the phase differences between EGG and fMRI time series.Figure 2

Selection of surface electrodes for electrogastrography and analysis of normal and abnormal electrogastrograms using Hjorth information

Electrogastrogram (EGG) signals recorded non-invasively using surface electrodes, represent the electrical activity of the stomach muscles and these signals are used to diagnose several digestive abnormalities. The surface electrodes play a significant role in the acquisition of EGG signals from human digestive system. In this work, an attempt has been made to demonstrate the role of contact area of surface electrodes for efficient measurement of EGG signals. Two different surface electrodes with contact diameter of 16 mm and 19 mm have been adopted for acquisition of EGG signals. Further, the Hjorth parameters of the EGG signals acquired from normal and abnormal cases suffering from diarrhoea, vomiting and stomach ulcer were analysed. Results demonstrate that the average values of activity, mobility and the complexity of the EGG signals increases with increase in contact area of the surface electrodes. Further, it is observed that there is a significant variation in Hjorth parameters for normal and abnormal cases.

Spectral analysis of electrogastrogram

Gastroparesis is a condition that affects the normal motility of muscle in the stomach and is commonly called as paralysis of stomach. Electrogastrography is the technique for measuring electrical activity of the stomach. The Electrogastrography is also used to measure the gastric mobility and various stomach disorders like tachy-gastria, brady-gastria, dyspepsia, peptic ulcer, gastric tumor etc. Electrogastrogram is the graphical representation of the Electrogastrography. The Electrogastrography procedure is recommended by the physician to diagnose the dis orders in the stomach. Electrogastrography procedure is taken generally under two conditions: Fasting condition and post prandial condition. This procedure is followed strictly to measure the gastric activity and gastric emptying test. The food is propelled into the stomach and the pacemaker of the stomach fails to contract the muscles of stomach that leads to gastroparesis condition. The diabetic patients often get into the gastroparesis condition. This remains as a major factor; stomach motility is slowed down. In this paper, data acquisition system for acquiring multichannel electrogastrogram is developed and the gastric signal is acquired and analysed using MATLAB. Through the spectral analysis of gastric slow-wave, the gastroparesis condition is diagnosed. The MEGG-Multichannel Channel Electrogastrogram signal is acquired in two states: Fasting state and Postprandial state.

A Combined Methodology to Eliminate Artifacts in Multichannel Electrogastrogram Based on Independent Component Analysis and Ensemble Empirical Mode Decomposition

Cutaneous measurements of electrogastrogram (EGG) signals are heavily contaminated by artifacts due to cardiac activity, breathing, motion artifacts, and electrode drifts whose effective elimination remains an open problem. A common methodology is proposed by combining independent component analysis (ICA) and ensemble empirical mode decomposition (EEMD) to denoise gastric slow-wave signals in multichannel EGG data. Sixteen electrodes are fixed over the upper abdomen to measure the EGG signals under three gastric conditions, namely, preprandial, postprandial immediately, and postprandial 2 h after food for three healthy subjects and a subject with a gastric disorder. Instantaneous frequencies of intrinsic mode functions that are obtained by applying the EEMD technique are analyzed to individually identify and remove each of the artifacts. A critical investigation on the proposed ICA-EEMD method reveals its ability to provide a higher attenuation of artifacts and lower distortion than those obtained by the ICA-EMD method and conventional techniques, like bandpass and adaptive filtering. Characteristic changes in the slow-wave frequencies across the three gastric conditions could be determined from the denoised signals for all the cases. The results therefore encourage the use of the EEMD-based technique for denoising gastric signals to be used in clinical practice.

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

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.

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.

한방 설문과 생체신호 간 연관분석 모델

The purpose of this research is to analyze the questionnaire of oriental medicine and bio-signal data and to identify the correlation between symptoms of oriental medicine and bio-signal in order to develop the customization model for individual patients. First, this research analyzes the questionnaire data using the developed questionnaire for oriental medicine symptoms and selects the optimized questionnaires for identifying the symptoms. Using data mining technologies with the selected questionnaires, this research divides the normal group and the group with symptoms. Finally, this research identifies the correlation between the questionnaire for oriental medicine symptoms and electrogastrogram signal.

Influence of body parameters on gastric bioelectric and biomagnetic fields in a realistic volume conductor

Electrogastrograms (EGG) and magnetogastrograms (MGG) provide two complementary methods for non-invasively recording electric or magnetic fields resulting from gastric electrical slow wave activity. It is known that EGG signals are relatively weak and difficult to reliably record while magnetic fields are, in theory, less attenuated by the low-conductivity fat layers present in the body. In this paper, we quantified the effects of fat thickness and conductivity values on resultant magnetic and electric fields using anatomically realistic torso models and trains of dipole sources reflecting recent experimental results. The results showed that when the fat conductivity was increased, there was minimal change in both potential and magnetic fields. However, when the fat conductivity was reduced, the magnetic fields were largely unchanged, but electric potentials had a significant change in patterns and amplitudes. When the thickness of the fat layer was increased by 30 mm, the amplitude of the magnetic fields decreased 10% more than potentials but magnetic field patterns were changed about four times less than potentials. The ability to localize the underlying sources from the magnetic fields using a surface current density measure was altered by less than 2 mm when the fat layer was increased by 30 mm. In summary, results confirm that MGG provides a favorable method over EGG when there are uncertain levels of fat thickness or conductivity.

English

A digestive system disorders are the very important problem in human being universally. As a panacea, a preliminary investigation method is proposed before the endoscopy procedure is called Electrogastrography. Electrogastrogram (EGG) is obtained from the human being cutaneously, which is non- invasive, easy procedure over endoscopy. EGG recorded for a 180 human being includes the normal individual and patients with digestive system disorder such us Nausea, Dyspepsia, Vomiting, ulcer, etc. A database is created for the analysis purpose. During the acquisition of EGG, the analog signal's numeric values are recorded as .bio file format using the data scope. Wavelet transform is used for the analysis of EGG to find the deviation in frequency and Power from the power spectrum estimate plot obtained for disorders patients compare to normal Individual. Wavelet Transform analysis includes the principle component analysis, denosing of the signal and power spectrum estimation. In the proposed wavelet analysis, the data of a patient or normal individual is included for analysis. The EGG signal is reconstructed with a data obtained from the data scope. This signal undergoes principle component analysis to remove the noise in the EGG signal to obtain de-noised signal and it is plotted for power spectral density estimation with Welch power spectral Density Estimation. As a result of the proposed method the power variation is found to be a range of 40-52dB and frequency is detected as 0.06 Hz to 0.075Hz for ulcer patient 0.02 Hz to 0.04 Hz for dyspepsia patient and 0.05Hz to 0.058 Hz for Normal Individual. The above said finding supports the physician in the diagnosis of digestive system disorders at fair amount of accuracy.

Portable EGG recording system based on a digital voice recorder

Cutaneous electrogastrogram (EGG) recording offers the benefit of non-invasive gastrointestinal diagnosis. With long-term ambulatory recording of signals, researchers and clinicians could have more opportunities to investigate and analyse paroxysmal or acute symptoms. A portable EGG system based on a digital voice recorder (DVR) is designed for long-term recording of cutaneous EGG signals. The system consists of electrodes, an EGG amplifier, a modulator, and a DVR. Online monitoring and off-line acquisition of EGG are handled by software. A special design employing an integrated timer circuit is used to modulate the EGG frequency to meet the input requirements of the DVR. This approach involves low supply voltage and low power consumption. Software demodulation is used to simplify the complexity of the system, and is helpful in reducing the size of the portable device. By using surface-mount devices (SMD) and a low-power design, the system is robust, compact, and suitable for long-term portable recording. As a result, researchers can record an ambulatory EGG signal by means of the proposed circuits in conjunction with an up-to-date voice-recording device.

Endoscopic Maneuvers of the Stomach Demonstrate Physiologic Characteristics of the Electrogastrogram

Purpose: Controversy over the significance of the Electrogastrogram (EGG) has continued to limit its clinical usefulness. We investigated the origins and significance of directly recorded EGG signals in response to physiologic maneuvers in patients with drug-refractory gastroparesis (GP). Methods: 10 female GP patients (mean age 43 years, GP etiology: 1 diabetic, 9 idiopathic) underwent permanent gastric electrical stimulation (GES) system implantation. Intraoperatively, after seromuscular electrode placement but prior to abdominal closure, EGG recordings were performed sequentially for at least five minutes during the following periods: stomach partial inflation during endoscopy (baseline = Base), endoscopic maximal insufflation (R1), desufflation (D1), re-insufflation (R2), and repeat desufflation (D2). EGG recordings were analyzed qualitatively for rhythm (regular = Reg Vs. irregular = Irreg) and amplitude (Equal or UnEqual) and quantitively for mean frequency (Freq, in CPM) and amplitude (Amp, in mV) as well as the Freq/Amp ratio (FAR). Results were compared for differences from baseline frequency (base to R1) and between R1 and R2 by paired t-tests. Results: After the initial Base period, the R2D2 maneuvers demonstrated more regularity and equal amplitude in the desufflation periods (D1 and D2). There was an initial decrease then stabilization in frequency (P= 0.002 for R1 from baseline and P= 0.04 between R1 and R2). In the insufflation and desufflation periods, a progressive decrease in amplitude was noted (P < 0.02 for R2 and D2 from baseline) and a gradual increase in the FAR were noted (P > 0.05) (see Table). Conclusion: Intraoperative serosal EGG recordings, when provoked by physiologic maneuvers, show reproducible characteristics in gastric electrical activity. EGG recordings of the relaxed stomach consistently display a more regular rhythm and equal amplitude than with the distended stomach. Maintenance of FAR both when relaxed and distended may be a measure of gastric homeostasis. Standardization of the EGG in terms of gastric distention may be needed for accurate physiologic interpretation.Table

Gastric motility measurement and evaluation of functional dyspepsia by a bio-impedance method

In order to investigate the complex course of the electrical and mechanical processes of functional dyspepsia (FD), it is necessary to extract gastric motility information on both electricity and mechanism. According to the clinical standardization, 36 volunteers with functional dyspepsia were selected. The signal processing device has been designed by Chongqing University of Posts and Telecommunications. Multi-resolution analysis (MRA) decomposed the two signals of impedance gastric motility (IGM) and electrogastrogram (EGG) collected from the body surface. The wavelet transform is addressed to separate the IGM and EGG signals from impedance signals due to breathing and blood flow. By means of the energy and frequency spectrum analysis technique, the signals can be classified according to the dominant power and dominant frequency. Some indices, such as frequencies of EGG and IGM, signal power spectrum and dynamic spectrum, the rates of rhythm and power for the normal EGG and IGM and so on, can also be calculated. The primary experiments of gastric motility measurement and evaluation are executed by including healthy humans (control group: CG) and patients with FD (pathologic group: PG). There are significant differences in the temporal-domain and frequency-domain properties between the two groups. The main frequency of the CG belongs to 2–4 CPM and is clear and very regular, while the main frequency of the PG is much disordered. The peak of the maximal power of the CG belongs to 2–4 CPM and 1–2 CPM for the PG. The percentage of normal frequency (PNF) for the CG is 0.704 ± 0.255 and 0.402 ± 1.145 for the PG. The frequency instability coefficient (FIC) for the CG is 0.182 ± 0.059 and 0.374 ± 0.086 for the PG. The percentage of normal power (PNP) for the CG is 0.592 ± 0.044 and 0.468 ± 0.142 for the PG. The power instability coefficient (PIC) for the CG is 1.576 ± 0.481 and 4.006 ± 0.711 for the PG. The results of the experiments show that the proposed method of impedance can be a potential tool for the noninvasive assessment of gastric motility under gastrointestinal physiology and pathology conditions.

Biomagnetic characterization of spatiotemporal parameters of the gastric slow wave

Certain gastric disorders affect spatiotemporal parameters of the gastric slow wave. Whereas the electrogastrogram (EGG) evaluates electric potentials to determine primarily temporal parameters, fundamental physical limitations imposed by the volume conduction properties of the abdomen suggest the evaluation of gastric magnetic fields. We used a multichannel superconducting quantum interference device magnetometer to study the magnetogastrogram (MGG) in 20 normal human subjects before and after a test meal. We computed the frequency and amplitude parameters of the gastric slow wave from MGG. We identified normal gastric slow wave activity with a frequency of 2.6 +/- 0.5 cycles per minute (cpm) preprandial and 2.8 +/- 0.3 cpm postprandial. In addition to frequency and amplitude, the use of surface current density mapping applied to the multichannel MGG allowed us to visualize the propagating slow wave and compute its propagation velocity (6.6 +/- 1.0 mm s(-1) preprandial and 7.4 +/- 0.4 mm s(-1) postprandial). Whereas MGG and EGG signals exhibited strong correlation, there was very little correlation between the MGG and manometry. The MGG not only records frequency dynamics of the gastric slow wave, but also characterizes gastric propagation. The MGG primarily reflects the underlying gastric electrical activity, but not its mechanical activity.

Estimating Gastric Rhythm Differences Using a Wavelet Method from the Electrogastrograms of Normal and Diabetic Subjects

Electrogastrogram (EGG) is the cutaneous recording of gastric electrical activity. Some patients with nausea, vomiting, early satiety, and patients with long term insulin‐dependent diabetes exhibit gastric electrical rhythm disturbances. We are interested in investigating the EGG to determine if abnormalities of the EGG occur in association with those gastric complaints. The aim of this document is to investigate long term diabetics with a history of such gastric dysrhythmia complaints in an effort to further characterize the frequency of abnormalities of the EGG in real clinical diabetic gastroparesis patients. For feature extraction purposes, we have performed Wavelet‐Packet analysis of EGG signals and have obtained power spectral density of wavelet‐packet details and approximation coefficients in diabetic patients suffering from gastric dysrhythmia (tachygastria, bradygastria) and healthy volunteers. Wavelet‐Packet analysis provides details of changes in frequency components at any time point, and enhanced interpretation of cutaneous EGG recordings. In conclusion, this paper has shown that the Wavelet‐Packet analysis method is very useful in the analysis of cutaneous EGG recordings, especially in detecting dysrhythmic events.

Non-invasive gastric motility monitor: fast electrogastrogram (fEGG)

We propose a new analysis method to extract the motility information from the electrogastrogram signal that has been recorded at a higher sampler rate than the conventional approaches. This technique utilizes a fourth order Butterworth bandpass filter in extracting the 50–80 cycles per minute (cpm) activity that was previously noted to represent the spike activity range of the cutaneous signals of dogs. Receiver operating characteristics (ROC) analyses have been applied to the processed data to compare the detection performance of our fEGG technique to the conventional approaches that use the slow wave as the reference. The areas under the ROC curves comparing the changes from postprandial stage to fed stage for the fEGG study was found to be 0.961 while for the slow wave it was 0.686. We offer our method as a complementary one to the existing methods.

Detection of gastric slow wave propagation from the cutaneous electrogastrogram.

The gastric slow wave is originated in the proximal stomach and propagates distally toward the pylorus. It determines the maximum frequency and propagation of gastric contractions. The aim of this study was to detect the propagation of the gastric slow wave from the surface electrogastrogram (EGG). The study was performed in 11 healthy subjects of normal weight. Gastric myoelectrical activity was recorded for 1 h in the fasting state with the use of a specially designed multichannel recording device that was composed of four identical amplifiers with cutoff frequencies of 1.8 and 16.0 cycles/min. Four active electrodes were placed on the abdomen along the gastric axis and were connected to a common reference electrode placed near the xiphoid process, yielding four-channel bipolar EGG signals. Cross-covariance analysis was performed to compute the time lag among the four channels. There was a time lag in EGG waveform between channels 1 and 4 (9.6 +/- 1.1 s); the average time during which the time lag was observed (>/=3 s) was 89.9 +/- 9.0%. There was a significant difference in the time lag among different adjacent channels (P < 0.04); the time lag observed between channels 3 and 4 was significantly smaller than that between channels 1 and 2 (P < 0.03). No correlation was found between the body mass index and the time lag between channels 1 and 4 (r = -0.31, P = 0.3). It was concluded that, with a multichannel recording device with identical multiamplifiers and an appropriate arrangement of abdominal electrodes, the propagation of the gastric slow wave could be identified from the EGG in healthy subjects. This method may be used to detect the coupling of the gastric slow wave noninvasively.