Effect of electrode contact area on the information content of the recorded electrogastrograms: An analysis based on Rényi entropy and Teager-Kaiser Energy

Alagumariappan, Paramasivam; Krishnamurthy, Kamalanand; Kandiah, Sundravadivelu; Ponnuswamy, Mannar Jawahar

doi:10.1515/pjmpe-2017-0007

Cited by 20 publications

(5 citation statements)

References 20 publications

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“…Many authors used commercial systems to acquire gastric signals for their research. For measurement, devices that especially constructed to acquire electrogastrogram ( 46 , 76 – 79 ), multipurpose polygraphs ( 54 , 80 ), or general bioamplifiers ( 34 , 81 , 82 ) are used. Many other authors were also interested in the design and creation of custom systems for measurement of electrogastrogram ( 10 , 11 , 13 , 83 – 89 ).…”

Section: Measurement Methodologymentioning

confidence: 99%

Electrogastrography measurement systems and analysis methods used in clinical practice and research: comprehensive review

Oczka,

Augustynek,

Penhaker

et al. 2024

Front. Med.

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Electrogastrography (EGG) is a non-invasive method with high diagnostic potential for the prevention of gastroenterological pathologies in clinical practice. In this study, a review of the measurement systems, procedures, and methods of analysis used in electrogastrography is presented. A critical review of historical and current literature is conducted, focusing on electrode placement, measurement apparatus, measurement procedures, and time-frequency domain methods of filtration and analysis of the non-invasively measured electrical activity of the stomach. As a result, 129 relevant articles with primary aim on experimental diet were reviewed in this study. Scopus, PubMed, and Web of Science databases were used to search for articles in English language, according to the specific query and using the PRISMA method. The research topic of electrogastrography has been continuously growing in popularity since the first measurement by professor Alvarez 100 years ago, and there are many researchers and companies interested in EGG nowadays. Measurement apparatus and procedures are still being developed in both commercial and research settings. There are plenty variable electrode layouts, ranging from minimal numbers of electrodes for ambulatory measurements to very high numbers of electrodes for spatial measurements. Most authors used in their research anatomically approximated layout with two++ active electrodes in bipolar connection and commercial electrogastrograph with sampling rate of 2 or 4 Hz. Test subjects were usually healthy adults and diet was controlled. However, evaluation methods are being developed at a slower pace, and usually the signals are classified only based on dominant frequency. The main review contributions include the overview of spectrum of measurement systems and procedures for electrogastrography developed by many authors, but a firm medical standard has not yet been defined. Therefore, it is not possible to use this method in clinical practice for objective diagnosis.Systematic Review Registrationhttps://www.prisma-statement.org/.

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Section: Measurement Methodologymentioning

confidence: 99%

Electrogastrography measurement systems and analysis methods used in clinical practice and research: comprehensive review

Oczka,

Augustynek,

Penhaker

et al. 2024

Front. Med.

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“…For isolation purposes, the third electrode was placed as ground, away from the stomach area. The acquired EGGs were amplified with an amplification system developed using IC AD624 [2] and were logged using LABVIEW hardware and software.…”

Section: Egg Signal Acquisitionmentioning

confidence: 99%

“…EGGs from all participants were acquired for a period of 10 minutes (Figures 1 and 2). All EGGs were preprocessed and analyzed using custom made functions in MATLAB R2011b [2].…”

Section: Egg Signal Acquisitionmentioning

confidence: 99%

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

Alagumariappan¹,

Krishnamurthy²,

Kandiah³

et al. 2020

Preprint

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BACKGROUND Electrogastrography (EGG) is a non-invasive electrophysiological measurement procedure followed to measure the frequency and promptness of gastric myoelectrical activity, which is normally considered to investigate the mechanisms of human digestive system. Diabetes can cause alterations in the process of digestion. OBJECTIVE The objective of this work is to extract and select potential informative features from recorded normal and diabetic electrogastrograms for diagnosis of diabetes using EGG signals. METHODS In this work, a total of thirty features of electrogastrograms measured from normal subjects and diabetic cases, were extracted. Further, twenty potential informative features were selected using Genetic Algorithm assisted feature selection process. The extracted features were analyzed for further classification of normal and diabetic EGG signals. RESULTS Results demonstrate that there are distinct variations between the EMG signals recorded from normal subjects and diabetic patients. The investigations reveal that the features namely Maragos fractal dimension and Hausdorff’s box-counting fractal dimension have high degree of correlation with the mobility of normal and diabetic electrogastrograms. CONCLUSIONS Based on the analysis on the extracted features, it is seen that the selected features are suitable for the design of automated classification systems for classification of normal and diabetic cases.

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“…The frequency of the bradygastria is between 1.0 and 2.6 cpm, while the frequency of the tachygastria is between 3.7 and 10 cpm (Alagumariappan et al, 2017). Various neuromuscular disorders of the stomach are included in the diabetic gastropathy spectrum.…”

Section: Introductionmentioning

confidence: 99%

Stomach Disorder Detection and Analysis using Hybrid Learning Vector Quantization with African Buffalo Optimization Algorithm

Baljon

2024

Int. j. math. eng. manag. sci.

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The human digestive system's electrical activity may be recorded noninvasively by Electrogastrography (EGG). Electrogastrograms are recordings of the electrical activity produced by the stomach muscles. EGG Several gastrointestinal disorders may be diagnosed and their severity measured using EGG signal properties. The literature has several contributions to the categorization of EGG signals. The majority of them make use of either the EGG's frequency or time data. The wide variety of EGG signals is a challenge for current automated categorization methods. Therefore, this study's objective is to develop a lightweight classifier that achieves high classification accuracy while using little processing resources. To acquire normal and abnormal EGG signals at a reasonable cost, a three-electrode measuring device is created here, with classification performed by a hybrid of Linear Vector Quantization and the African Buffalo Search Algorithm (HLVQ-ASO). The results show that the information richness of recorded EGG signals from healthy persons is greater for EGG signals captured using a surface electrode with a contact diameter of 19 mm as compared to 16 mm. To demonstrate their validity and degree of classification accuracy, the results computed using the suggested classifiers are compared with the current classifiers like Artificial Neural Network, Multimodal Support Vector Machine (MSVM), and Improved Convolutional Neural Network (CNN). Additionally, the HLVQ-ASO-based classification method is effective in differentiating between normal and diabetic EGG signals, found a sensitivity of 97% and a specificity of 98.8%. For a dataset of 500 samples, the classification accuracy is 97%.

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Effect of electrode contact area on the information content of the recorded electrogastrograms: An analysis based on Rényi entropy and Teager-Kaiser Energy

Cited by 20 publications

References 20 publications

Electrogastrography measurement systems and analysis methods used in clinical practice and research: comprehensive review

Electrogastrography measurement systems and analysis methods used in clinical practice and research: comprehensive review

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

Stomach Disorder Detection and Analysis using Hybrid Learning Vector Quantization with African Buffalo Optimization Algorithm

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