• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利：CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法，用中文像聊天一样搜索，搜遍4000万医学文献。AI智能推荐，让科研检索更轻松。

立即免费搜索

文件翻译

保留排版，准确专业，支持PDF/Word/PPT等文件格式，支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述，25分钟生成高质量综述，智能提取关键信息，辅助科研写作。

立即免费体验

基于机器学习算法的生物传感器辅助腹部综合征分类方法。

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

机构信息

Department of CSE & IT, Jaypee Institute of Information Technology, Noida, India.

College of Engineering and Computing, Al Ghurair University, Dubai, UAE.

出版信息

Comput Intell Neurosci. 2022 Jan 28;2022:4454226. doi: 10.1155/2022/4454226. eCollection 2022.

DOI:10.1155/2022/4454226
PMID:35126492
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8816582/
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 (). 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%.

摘要

消化系统是人体生理学中必不可少的系统之一，胃在其中起着重要的作用，其附属器官包括食管、十二指肠、小肠和大肠。全球许多人患有胃节律紊乱，同时伴有消化不良（消化功能异常）、不明原因的恶心（感觉）、呕吐、腹部不适、胃溃疡和胃食管反流病。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。胃电图是记录通过胃肌肉传递的电脉冲并调节肌肉收缩的一种方法。电极感测来自胃肌肉的电脉冲，并记录胃电图。计算机分析捕获的胃电图（EGG）信号。正常的电节律在餐后会在典型的胃肌肉中产生增强的电流。餐后电节律异常表明胃肌肉或神经异常。本研究考虑了普通个体、心动过缓、消化不良、恶心、心动过速、溃疡和呕吐的 EGG 进行分析。数据是与医生合作收集的，用于分析患有疾病和日常个体的餐前和餐后条件。在使用遗传算法的 CWT 中，使用 db4 在 MATLAB 中以 3D 图的形式获取 EGG 信号波模式。该图显示，峰值的存在反映了 EGG 信号周期。存在的峰值数量对 EGG 进行分类。自适应共振分类器网络（ARCN）用于根据警觉参数（）识别 EGG 信号是正常还是异常。本研究可以作为一种诊断消化系统问题的医学工具，在提出侵入性治疗之前使用。所提出工作的准确性达到 95.45%，并且添加了灵敏度和特异性范围，分别为 92.45%和 87.12%。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6bd1/8816582/4dba01314b57/CIN2022-4454226.016.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6bd1/8816582/f420b3906d75/CIN2022-4454226.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6bd1/8816582/ba5659dad7e5/CIN2022-4454226.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6bd1/8816582/583d922c03d2/CIN2022-4454226.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6bd1/8816582/199e01ba3505/CIN2022-4454226.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6bd1/8816582/f829931cd90b/CIN2022-4454226.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6bd1/8816582/a87fee438149/CIN2022-4454226.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6bd1/8816582/472752505c9a/CIN2022-4454226.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6bd1/8816582/e8a09f794966/CIN2022-4454226.008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6bd1/8816582/60383bb2f724/CIN2022-4454226.009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6bd1/8816582/7355a7f7e703/CIN2022-4454226.010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6bd1/8816582/30b431c89777/CIN2022-4454226.011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6bd1/8816582/c14941fd9349/CIN2022-4454226.012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6bd1/8816582/c5d3d740f8b8/CIN2022-4454226.013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6bd1/8816582/ca82d63d3d6b/CIN2022-4454226.014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6bd1/8816582/64c5d6b5835d/CIN2022-4454226.015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6bd1/8816582/4dba01314b57/CIN2022-4454226.016.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6bd1/8816582/f420b3906d75/CIN2022-4454226.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6bd1/8816582/ba5659dad7e5/CIN2022-4454226.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6bd1/8816582/583d922c03d2/CIN2022-4454226.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6bd1/8816582/199e01ba3505/CIN2022-4454226.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6bd1/8816582/f829931cd90b/CIN2022-4454226.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6bd1/8816582/a87fee438149/CIN2022-4454226.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6bd1/8816582/472752505c9a/CIN2022-4454226.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6bd1/8816582/e8a09f794966/CIN2022-4454226.008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6bd1/8816582/60383bb2f724/CIN2022-4454226.009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6bd1/8816582/7355a7f7e703/CIN2022-4454226.010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6bd1/8816582/30b431c89777/CIN2022-4454226.011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6bd1/8816582/c14941fd9349/CIN2022-4454226.012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6bd1/8816582/c5d3d740f8b8/CIN2022-4454226.013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6bd1/8816582/ca82d63d3d6b/CIN2022-4454226.014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6bd1/8816582/64c5d6b5835d/CIN2022-4454226.015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6bd1/8816582/4dba01314b57/CIN2022-4454226.016.jpg

相似文献

1
Biosensor-Assisted Method for Abdominal Syndrome Classification Using Machine Learning Algorithm.基于机器学习算法的生物传感器辅助腹部综合征分类方法。
Comput Intell Neurosci. 2022 Jan 28;2022:4454226. doi: 10.1155/2022/4454226. eCollection 2022.
2
Gastric electrical stimulation: an evidence-based analysis.胃电刺激：基于证据的分析。
Ont Health Technol Assess Ser. 2006;6(16):1-79. Epub 2006 Aug 1.
3
Analysis of gastric myoelectrical activity from the electrogastrogram signals based on wavelet transform and line length feature.基于小波变换和线长特征的胃电图信号胃肌电活动分析
Proc Inst Mech Eng H. 2018 Apr;232(4):403-411. doi: 10.1177/0954411918757812. Epub 2018 Feb 14.
4
Abnormalities of the electrogastrogram in functional gastrointestinal disorders.功能性胃肠病的胃电图异常
Am J Gastroenterol. 1999 Apr;94(4):1023-8. doi: 10.1111/j.1572-0241.1999.01007.x.
5
[Relationship between electrogastrogram and gastric emptying in children with functional dyspepsia].[功能性消化不良患儿胃电图与胃排空的关系]
Zhonghua Er Ke Za Zhi. 2010 Apr;48(4):249-53.
6
Electrogastrography before and after a high-caloric, liquid test meal in healthy volunteers and patients with severe functional dyspepsia.健康志愿者和重度功能性消化不良患者在摄入高热量流质试验餐前后的胃电图检查。
Scand J Gastroenterol. 2002 Oct;37(10):1144-8. doi: 10.1080/003655202760373344.
7
Gastric myoelectrical activity, gastric emptying and correlations with dyspepsia symptoms in patients with gastroesophageal reflux.胃食管反流病患者的胃肌电活动、胃排空及其与消化不良症状的相关性
J Investig Med. 1997 Oct;45(8):483-7.
8
The effect of cisapride on dyspepsia symptoms and the electrogastrogram in patients with non-ulcer dyspepsia.西沙必利对非溃疡性消化不良患者消化不良症状及胃电图的影响。
Aliment Pharmacol Ther. 1998 Aug;12(8):755-9. doi: 10.1046/j.1365-2036.1998.00369.x.
9
Spatial Patterns From High-Resolution Electrogastrography Correlate With Severity of Symptoms in Patients With Functional Dyspepsia and Gastroparesis.高分辨率体表胃电图的空间模式与功能性消化不良和胃轻瘫患者症状严重程度相关。
Clin Gastroenterol Hepatol. 2019 Dec;17(13):2668-2677. doi: 10.1016/j.cgh.2019.04.039. Epub 2019 Apr 19.
10
[Electrogastograph during interdigestive and digestive stages in patients with functional dyspepsia].[功能性消化不良患者消化间期和消化期的胃电图]
Zhongguo Yi Xue Ke Xue Yuan Xue Bao. 2000 Jun;22(3):245-9.

引用本文的文献

1
Electrogastrography measurement systems and analysis methods used in clinical practice and research: comprehensive review.临床实践和研究中使用的胃电图测量系统及分析方法：综述
Front Med (Lausanne). 2024 Jul 1;11:1369753. doi: 10.3389/fmed.2024.1369753. eCollection 2024.
2
Prediction of gastrointestinal functional state based on myoelectric recordings utilizing a deep neural network architecture.基于深度神经网络架构的肌电记录胃肠功能状态预测。
PLoS One. 2023 Jul 27;18(7):e0289076. doi: 10.1371/journal.pone.0289076. eCollection 2023.
3
Preserving the Privacy of Healthcare Data over Social Networks Using Machine Learning.

本文引用的文献

1
Efficient deep learning approach for augmented detection of Coronavirus disease.用于增强型冠状病毒病检测的高效深度学习方法。
Neural Comput Appl. 2022;34(14):11423-11440. doi: 10.1007/s00521-020-05410-8. Epub 2021 Jan 19.
2
A Robust Quasi-Quantum Walks-Based Steganography Protocol for Secure Transmission of Images on Cloud-Based E-healthcare Platforms.基于鲁棒拟量子游走的图像隐写协议用于基于云的电子医疗保健平台上的安全图像传输。
Sensors (Basel). 2020 May 31;20(11):3108. doi: 10.3390/s20113108.
3
Comparison of artificial neural networks an support vector machines for feature selection in electrogastrography signal processing.
利用机器学习保护社交网络中的医疗保健数据隐私。
Comput Intell Neurosci. 2022 May 5;2022:4690936. doi: 10.1155/2022/4690936. eCollection 2022.
人工神经网络与支持向量机在胃电图信号处理中特征选择的比较
Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:2774-7. doi: 10.1109/IEMBS.2010.5626362.
4
Rhythmic and spatial abnormalities of gastric slow waves in patients with functional dyspepsia.功能性消化不良患者胃慢波的节律性和空间异常。
J Clin Gastroenterol. 2009 Feb;43(2):123-9. doi: 10.1097/MCG.0b013e318157187a.
5
Multichannel electrogastrography under a magnifying glass--an in-depth study on reproducibility of fed state electrogastrograms.放大镜下的多通道胃电图——进食状态胃电图重现性的深入研究
Neurogastroenterol Motil. 2008 Jun;20(6):625-34. doi: 10.1111/j.1365-2982.2008.01087.x. Epub 2008 Feb 19.
6
Texture analysis and classification with tree-structured wavelet transform.基于树状小波变换的纹理分析与分类。
IEEE Trans Image Process. 1993;2(4):429-41. doi: 10.1109/83.242353.
7
Performance evaluation of a sequential minimal radial basis function (RBF) neural network learning algorithm.一种序贯最小径向基函数（RBF）神经网络学习算法的性能评估
IEEE Trans Neural Netw. 1998;9(2):308-18. doi: 10.1109/72.661125.
8
Genetic programming for simultaneous feature selection and classifier design.用于同时进行特征选择和分类器设计的遗传编程。
IEEE Trans Syst Man Cybern B Cybern. 2006 Feb;36(1):106-17. doi: 10.1109/tsmcb.2005.854499.
9
Estimation of wavelet and short-time Fourier transform sonograms of normal and diabetic subjects' electrogastrogram.正常人和糖尿病患者胃电图的小波变换和短时傅里叶变换声谱图估计
Comput Biol Med. 2006 Dec;36(12):1289-302. doi: 10.1016/j.compbiomed.2005.07.005. Epub 2005 Nov 2.
10
Neural classification of lung sounds using wavelet coefficients.基于小波系数的肺音神经分类
Comput Biol Med. 2004 Sep;34(6):523-37. doi: 10.1016/S0010-4825(03)00092-1.