Comparison of artificial neural networks an support vector machines for feature selection in electrogastrography signal processing

Curilem, Millaray; Chacón, Máx; Acuña, Gonzalo; Ulloa, Sebastián; Pardo, Carlos; Defilíppí, C; Madrid, Ana María

doi:10.1109/iembs.2010.5626362

Cited by 2 publications

(1 citation statement)

References 17 publications

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“…To compare the sensitivity of different methods, an analysis of specification is performed, and 96% accuracy of classification occurred using BP-MRAN with the combination of a robust algorithm of backpropagation. It was found that the BP-MRAN with trainrp improved performance by 14 percent and 10 percent, respectively, in comparison to the results obtained by Chacon et al using the BPANN in a combination of trainrp [54] and the results obtained by Curilem et al using the GA and SVM [55].…”

Section: Resultsmentioning

confidence: 52%

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

Gandhi

Ahmad

Mehbodniya

et al. 2022

Computational Intelligence and Neuroscience

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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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Section: Resultsmentioning

confidence: 52%

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

Gandhi

Ahmad

Mehbodniya

et al. 2022

Computational Intelligence and Neuroscience

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Data augmentation for generating synthetic electrogastrogram time series

Miljković,

Milenić,

Popović

et al. 2024

Med Biol Eng Comput

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To address an emerging need for large number of diverse datasets for rigor evaluation of signal processing techniques, we developed and evaluated a new method for generating synthetic electrogastrogram time series. We used electrogastrography (EGG) data from an open database to set model parameters and statistical tests to evaluate synthesized data. Additionally, we illustrated method customization for generating artificial EGG time series alterations caused by the simulator sickness. Proposed data augmentation method generates synthetic EGG data with specified duration, sampling frequency, recording state (postprandial or fasting state), overall noise and breathing artifact injection, and pauses in the gastric rhythm (arrhythmia occurrence) with statistically significant difference between postprandial and fasting states in > 70% cases while not accounting for individual differences. Features obtained from the synthetic EGG signal resembling simulator sickness occurrence displayed expected trends. The code for generation of synthetic EGG time series is not only freely available and can be further customized to assess signal processing algorithms but also may be used to increase data diversity for training artificial intelligence (AI) algorithms. The proposed approach is customized for EGG data synthesis but can be easily utilized for other biosignals with similar nature such as electroencephalogram. Graphical abstract

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Comparison of artificial neural networks an support vector machines for feature selection in electrogastrography signal processing

Cited by 2 publications

References 17 publications

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

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

Data augmentation for generating synthetic electrogastrogram time series

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