Detection of delayed gastric emptying from electrogastrograms with support vector machine

Author

Liang, Hualou ; Lin, Zhiyue

Author_Institution

Center for Complex Syst., Florida Atlantic Univ., Boca Raton, FL, USA

Volume

48

Issue

5

fYear

2001

fDate

5/1/2001 12:00:00 AM

Firstpage

601

Lastpage

604

Abstract

A recent study reported a conventional neural network (NN) approach for the noninvasive diagnosis of delayed gastric emptying from the cutaneous electrogastrograms. Using support vector machine, we show that this relatively new technique can be used for detection of delayed gastric emptying and is in fact able to outdo the conventional NN.

Keywords

electromyography; generalisation (artificial intelligence); learning (artificial intelligence); medical signal processing; neural nets; pattern classification; signal classification; spectral analysis; cutaneous EGG; delayed gastric emptying; electrogastrograms; feature selection; generalisation error; myoelectric activity; neural network approach; noninvasive diagnosis; pattern classification; quadratic programming; spectral analysis; support vector machine; Abdomen; Delay; Frequency; Neural networks; Noninvasive treatment; Spectral analysis; Stomach; Support vector machine classification; Support vector machines; Testing; Algorithms; Diagnosis, Computer-Assisted; Electrophysiology; Gastric Emptying; Humans; Models, Biological; Neural Networks (Computer); Stomach Diseases;

fLanguage

English

Journal_Title

Biomedical Engineering, IEEE Transactions on

Publisher

ieee

ISSN

0018-9294

Type

jour

DOI

10.1109/10.918600

Filename

918600