Biosensor-Assisted Method for Abdominal Syndrome Classification Using Machine Learning Algorithm.
Biosensor-Assisted Method for Abdominal Syndrome Classification Using Machine Learning Algorithm.
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DOI：
10.1155/2022/4454226
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发表时间：
2022
影响因子：
--
通讯作者：
Tiwari B
中科院分区：
文献类型：
--
作者：
Gandhi C;Ahmad SS;Mehbodniya A;Webber JL;Hemalatha S;Elwahsh H;Tiwari B
消化系统是人体生理学中的基本系统之一，其中胃与其附件如食道、十二指肠、小肠和大肠一起发挥重要作用。地球仪上的许多人患有胃节律障碍，并伴有消化不良（消化不良）、不明原因的恶心（感觉）、呕吐、腹部不适、胃溃疡和胃食管反流疾病。用于识别异常的一些技术包括临床分析、内窥镜检查、胃电图和成像。胃电图是通过胃部肌肉和调节肌肉收缩的电脉冲的记录。电极感知来自胃部肌肉的电脉冲，并记录胃电图。计算机分析捕获的胃电图（EGG）信号。通常的电节律在饭后典型的胃部肌肉中产生增强的电流。餐后电节律在胃部肌肉或神经异常的患者中是异常的。本研究考虑普通个体的EGG、心动过缓、消化不良、恶心、心动过速、溃疡和呕吐进行分析。与医生合作收集数据，用于疾病患者和日常个人的餐前和餐后情况。在基于遗传算法的连续小波变换中，利用db4算法，利用MATLAB绘制了三维图，得到了胃电信号的波形图。该图表明，峰值的存在反映了EGG信号周期。目前的峰的数量分类EGG。自适应共振分类器网络（ARCN）用于根据警觉性（μ）参数将EGG信号识别为正常或异常对象。这项研究可以作为一种医疗工具，在提出侵入性治疗之前诊断消化系统问题。本工作的准确性达到95.45%，敏感性和特异性范围增加到92.45%和87.12%。
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%.
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