Detection of gastric dysrhythmia using WT and ANN in diabetic gastroparesis patients

Gastric myoelectrical activity can be measured by a noninvasive technique called electrogastrography where surface electrodes are placed on the epigastric area of the abdomen. The electrogastrogram (EGG) signal is by nature a nonstationary signal in terms of its frequency, amplitude and wave shape....

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Veröffentlicht in:Computers in biology and medicine 2006-03, Vol.36 (3), p.276-290
Hauptverfasser: Kara, Sadık, Dirgenali, Fatma, Okkesim, Şükrü
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creator Kara, Sadık
Dirgenali, Fatma
Okkesim, Şükrü
description Gastric myoelectrical activity can be measured by a noninvasive technique called electrogastrography where surface electrodes are placed on the epigastric area of the abdomen. The electrogastrogram (EGG) signal is by nature a nonstationary signal in terms of its frequency, amplitude and wave shape. Unlike the other methods discrete wavelet analysis (DWT) was designed for nonstationary signals. For automatic assessment of EGG, we used artificial neural networks (ANNs) that have been widely employed in pattern recognition due to their great potential of high performance, flexibility, robust fault tolerance, cost-effective functionality and capability for real-time applications. So we developed a new method for classification of EGG based on DWT and ANN.
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subjects Adolescent
Adult
Artificial neural network
Computer applications
Diabetes Mellitus - physiopathology
Diagnosis
Electrocardiography
Electrogastrography
Female
Gastric electrical dysrhythmia
Gastroparesis - diagnosis
Gastroparesis - physiopathology
Health care
Humans
Male
Medicine
Neural networks
Neural Networks (Computer)
Predictive Value of Tests
Sensitivity and Specificity
Spectral analysis
Wavelet transform
title Detection of gastric dysrhythmia using WT and ANN in diabetic gastroparesis patients
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