Abstract
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%.
Highlights
Human physiology comprises the nervous system, cardiovascular system, respiratory system, and digestive system.e digestive system, among these systems, is one of the most powerful systems where the stomach plays a vital part with its accessories such as the esophagus, duodenum, small intestines, and large intestines. e digestive system consists of the gastrointestinal tract, the mouth twisting pipe to the anus, and other organizations that assist the body to break down and absorb food
Electrogastrogram is the registration of electrical impulses that pass through the stomach muscles and regulate the contraction of the muscle. e electrode senses electrical impulses from the stomach muscles, and the EGG is recorded to investigate digestive system problems. e research is carried out with diseases such as bradygastria, dyspepsia, nausea, tachygastria, ulcers, and vomiting
Three artificial neural network (ANN) architectures have been built and tested to categorize EGG signals. e EGG is classified as normal or abnormal using the ARCNNN network, an unsupervised network. e Learning Vector Quantization (LVQ) network is investigated as a supervised method that employs competing layers to improve the accuracy of the classification decision-making process. e BPNN was implemented via the use of supervised learning
Summary
Human physiology comprises the nervous system, cardiovascular system, respiratory system, and digestive system.e digestive system, among these systems, is one of the most powerful systems where the stomach plays a vital part with its accessories such as the esophagus, duodenum, small intestines, and large intestines. e digestive system consists of the gastrointestinal tract, the mouth twisting pipe to the anus, and other organizations that assist the body to break down and absorb food. E electrode senses electrical impulses from the stomach muscles, and the EGG is recorded to investigate digestive system problems. EGG data are evaluated using statistical parameters, method of wavelet transformation, and approach to the neural network. Is technique gave better precision and more accurate information regarding frequency changes in electric stomach activity. It is beneficial for identifying short-term dysrhythmic occurrences of stomach activity. Ding et al employed an electrogastrography to detect slow stomach waves, and the authors developed a multiresolution technique to deconstruct the EGG signal using the Daubechies wavelet function [11]. Zhenghu has created a novel wavelet-based treatment technique of EGG signals with an excellent application viewpoint. Kania et al have investigated the significance of the proper selection of mom’s wavelet for decomposing the ECG signal noise [13]. e researchers concluded they got a high-quality signal on the first and fourth degradation levels for the wavelet db and sym for the fourth degradation level
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