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

2020 ◽  
Vol 10 (6) ◽  
pp. 1281-1287
Author(s):  
Bin Wang ◽  
Tingxi Wen ◽  
Jigong Hou ◽  
Luxin Lin ◽  
Yan Fang ◽  
...  
Keyword(s):  
Gastric Motility ◽  
Gastrointestinal Hormones ◽  
Gastrointestinal Diseases ◽  
Parameter Analysis ◽  
High Dimensional ◽  
Efficacy Evaluation ◽  
Non Invasive ◽  
Signal Collection ◽  
Egg Signal ◽  
Relative Change

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.

scholarly journals Somatostatin and gastrointestinal tract. Clinical experiences

2013 ◽  
Vol 154 (39) ◽  
pp. 1535-1540 ◽  
Author(s):  
László Herszényi ◽  
Emese Mihály ◽  
Zsolt Tulassay
Keyword(s):  
Gastrointestinal Tract ◽  
Gastrointestinal Hormones ◽  
Gastrointestinal Diseases ◽  
Somatostatin Analogues ◽  
Therapeutic Modality ◽  
Exocrine Function ◽  
Somatostatin Analogue ◽  
Clinical Experiences ◽  
Pancreatic Fistulas ◽  
Long Acting

The effect of somatostatin on the gastrointestinal tract is complex; it inhibits the release of gastrointestinal hormones, the exocrine function of the stomach, pancreas and bile, decreases motility and influences absorption as well. Based on these diverse effects there was an increased expectation towards the success of somatostatin therapy in various gastrointestinal disorders. The preconditions for somatostatin treatment was created by the development of long acting somatostatin analogues (octreotide, lanreotide). During the last twenty-five years large trials clarified the role of somatostatin analogues in the treatment of various gastrointestinal diseases. This study summarizes shortly these results. Somatostatin analogue treatment could be effective in various pathological conditions of the gastrointestinal tract, however, this therapeutic modality became a part of the clinical routine only in neuroendocrine tumours and adjuvant treatment of oesophageal variceal bleeding and pancreatic fistulas. Orv. Hetil., 2013, 154, 1535–1540.

A non-invasive technique for gastric motility measurement

1977 ◽  
Vol 50 (595) ◽  
pp. 526-527 ◽  
Author(s):  
D. N. Bateman ◽  
S. Leeman ◽  
C. Metreweli ◽  
K. Willson
Keyword(s):  
Gastric Motility ◽  
Invasive Technique ◽  
Non Invasive

scholarly journals A survey of video capsule endoscope system based on wireless energy transfer technology

2020 ◽  
Vol 309 ◽  
pp. 01007
Author(s):  
Ting Fang ◽  
Xiongwei Wu
Keyword(s):  
Gastrointestinal Diseases ◽  
Frame Rate ◽  
Internal Space ◽  
Capsule Endoscope ◽  
Non Invasive ◽  
Detection Technology ◽  
Endoscope System ◽  
Wireless Capsule ◽  
Time Image

The wireless capsule endoscope system allows clinicians to directly observe the image of the inner wall of the human gastrointestinal tract and obtain the most intuitive information at the lesion. Compared with traditional endoscopic detection technology, its painless, non-invasive, safe and convenient, full gastrointestinal detection features make this technology a research focus in the field of medical devices at home and abroad. As a new non-invasive detection technology for gastrointestinal diseases, the working time, image frame rate and image quality of the existing wireless capsule endoscope system cannot fully meet the needs in clinical applications. The cause of these problems lies in the capsule. The internal space of the speculum is limited, and only the button battery can be used as the energy source. Therefore, it is necessary to design a video capsule endoscope based on wireless energy transmission technology. This article is a review of Zhu Zhanquan, Xue Kaifeng, Lu Ruiqi and other scholars.

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

2021 ◽  
Vol 23 (05) ◽  
pp. 286-293
Author(s):  
Gokul M ◽  
◽  
Jothiraj S ◽  
Pradeep Murugesan ◽  
Monisha R ◽  
...  
Keyword(s):  
Compression Ratio ◽  
Wavelet Transforms ◽  
Graphical Representation ◽  
Transmission Rate ◽  
Signal Compression ◽  
Signal Loss ◽  
Compression Performance ◽  
Non Invasive ◽  
Egg Signal ◽  
Mean Square Difference

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.

Occult blood fecal tests for self-control and point of care testing

2017 ◽  
Vol 53 (2) ◽  
pp. 101-106
Author(s):  
Aleksandra Charchut ◽  
Magdalena Wójcik ◽  
Barbara K. Kościelniak ◽  
Przemysław J. Tomasik
Keyword(s):  
Point Of Care ◽  
Occult Blood ◽  
Gastrointestinal Diseases ◽  
Fecal Occult Blood Testing ◽  
Self Control ◽  
Fecal Occult Blood ◽  
Point Of Care Testing ◽  
Non Invasive ◽  
Fecal Occult ◽  
Fecal Tests

Fecal occult blood testing (FOBT) is a non-invasive and easy-to-carry, self-performed assay. It is often conducted in the diagnosis of various gastrointestinal diseases, especially as a screening test for colorectal cancer. This test is aimed at detecting blood which is not visible macroscopically in a stool sample. The purpose of this paper is to discuss various types of FOB tests: chemical, immunochroma- tographic and DNA tests. Despite the similarity in their performance, these tests use different methods and thus differ in their ability to detect blood from different parts of the gastrointestinal tract. In addition, the interfering factors in the various assays and the proper preparation of the patient before the test are discussed in detail. The knowledge of the differences between these tests will allow to correct performance and interpretation of the results obtained with each tests.

Development of non-invasive blood glucose regression based on near-infrared spectroscopy combined with a deep learning method

2021 ◽  
Author(s):  
Zhuyu Wang ◽  
Linhua Zhou ◽  
Tianqing Liu ◽  
Kewei Huan ◽  
Xiaoning Jia
Keyword(s):  
Infrared Spectroscopy ◽  
Blood Glucose ◽  
Near Infrared Spectroscopy ◽  
Prediction Accuracy ◽  
Near Infrared ◽  
Blood Glucose Concentration ◽  
High Dimensional ◽  
Support Vector ◽  
Non Invasive ◽  
Blood Glucose Concentrations

Abstract Extracting micro-scale spectral features from dynamic blood glucose concentrations is extremely difficult when using non-invasive measurement methods. This work proposes a new machine-learning method based on near-infrared spectroscopy, deep belief network (DBN), and support vector machine (SVR), to improve the prediction accuracy. First, the standard oral glucose tolerance test is used to collect near-infrared spectroscopy and actual blood glucose concentration values for specific wavelengths (1200, 1300, 1350, 1450, 1600, 1610, and 1650 nm), and the blood glucose concentrations is within a clinical range of 70mg/dL~220mg/dL. Second, based on the DBN model, high-dimensional deep features of the non-invasive blood glucose spectrum are extracted. These are used to establish a support vector regression (SVR) model and to quantitatively analyze the influence of spectral sample size and corresponding feature dimensions (i.e., DBN network structure) on the prediction accuracy. Finally, based on data from six volunteers, a comparative analysis of the SVR prediction accuracy is performed both before and after using high-dimensional deep features. For volunteer 1, when the DBN-based high-dimensional deep features were used, the root mean square error (RMSE) of support vector regression (SVR) was reduced by 71.67%, the correlation coefficient (R2) and the P value of Clark grid analysis (P) were increased by 13.99% and 6.28%, respectively. Moreover, we have similar results when the proposed method was carried out on the data of other volunteers. The results show that the presented algorithm can play an important role in dynamic non-invasive blood glucose concentration prediction and can effectively improve the accuracy of the SVR model. Further, by applying the algorithm to six independent sets of data, this research also illustrates the high-precision regression and generalization capabilities of the DBN-SVR algorithm.

62 A Novel Non-Invasive Method to Monitor Gastric Motility in Mice: Role of CRF During Cold Stress

2008 ◽  
Vol 134 (4) ◽  
pp. A-9
Author(s):  
Guillaume Gourcerol ◽  
David W. Adelson ◽  
Mulugeta Million ◽  
Lixin Wang ◽  
Yvette Tache
Keyword(s):  
Cold Stress ◽  
Gastric Motility ◽  
Non Invasive ◽  
Invasive Method
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