TRANSCUTANEOUS MULTICHANNEL ELECTROGASTROGRAPHY: NORMAL PARAMETERS IN A BRAZILIAN POPULATION

ABSTRACT BACKGROUND: Electrogastrography (EGG) is a noninvasive technique for the assessment of gastric myoelectrical activity using electrodes placed on the abdominal surface. Changes in gastric myoelectrical activity may be associated with diseases such as gastroparesis, functional dyspepsia, nausea, and recurrent vomiting. In Brazil, no studies to date have assessed gastric myoelectrical activity using multichannel EGG in healthy individuals. OBJECTIVE: To establish normal values of transcutaneous multichannel EGG in healthy Brazilian individuals. METHODS: This was a prospective study including 20 healthy individuals who underwent EGG. Recording was performed during two periods: a preprandial recording was performed for 30 minutes, and a postprandial recording was performed for 30 minutes after a soft-solid meal of 400 kcal (20 grams of proteins, 60 grams of carbohydrates, and 9 grams of fat). RESULTS: We assessed dominant frequency (DF) parameters, %DF distribution, the instability coefficient, and the power ratio (PR). A total of 20 individuals (11 women and 9 men) with a mean age of 39.5±7.4 years were included. Mean DF (95%CI) ranged from 2.4 to 3.1 cpm in the resting phase and 2.6 to 3.2 cpm in the postprandial period. The %DF in normogastria range was >70% in all healthy individuals. We identified that only one individual did not present a positive response to the test meal, and the other 19 individuals showed a PR greater than 1. The instability coefficient did not change significantly with meal intake. CONCLUSION: Multichannel EGG may be applied in future studies to evaluate gastric motility disorders in the Brazilian population.

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.

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

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.

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.

Gastric Myoelectrical Activity and Autonomic Nervous System Abnormalities in Patients with Chronic Unexplained Nausea and Vomiting and in Patients with Gastroparesis

Background: Patients with chronic unexplained nausea and vomiting (CUNV) and Gastroparesis (GP) have similar symptoms, suggesting they share pathophysiological abnormalities along a continuum of disease.Objectives: To determine the incidence of gastric myoelectrical, accommodation dysfunction and autonomic abnormalities in patients with CUNV and GP.Methods: Outpatients with CUNV and GP who underwent standard 4-hr solid phase gastric emptying, upright tilt table test and electrogastrogram (EGG) recordings with water load satiety test (WLST) were identified from chart review. Subjects with normal emptying were in the CUNV group; those with delayed emptying were in the GP group. EGGs were recorded before and 30 minutes after the WLST and symptoms were recorded on a 100mm visual analog scale.Results: 44 patients (35 women and 9 men, ages 17-76 years) were identified: 24 had normal gastric emptying and CUNV and 20 had GP. Gastric dysrhythmias were found in 70% of CUNV and 69% of GP patients. Twenty percent of CUNV patients and 44% of GP patients ingested abnormally low volumes (< 300mL) during the WLST. Nausea increased similarly after the WLST in the subjects with CUNV and GP (Ps > 0.05). Postural orthostatic tachycardia syndrome (POTS) was diagnosed in 17% of CUNV patients and 20% of GP patients.Conclusions: Gastric myoelectrical and accommodation abnormalities and autonomic nervous system (ANS) dysfunctions frequently occur in subjects with CUNV and GP. These pathophysiological abnormalities support the idea that CUNV and GP occur along the same continuum of gastric neuromuscular dysfunction and may be targets for therapeutic approaches.