论文信息 - Detection of delayed gastric emptying from electrogastrograms with support vector machine

Detection of delayed gastric emptying from electrogastrograms with support vector machine

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.

Hualou Liang | Zhiyue Lin | Hualou Liang | Zhiyue Lin

[1] Richard W. McCallum,et al. Noninvasive feature-based detection of delayed gastric emptying in humans using neural networks , 2000, IEEE Transactions on Biomedical Engineering.

[2] Michael Camilleri,et al. Gastric electromechanical function and gastric emptying in diabetic gastroparesis , 1991 .

[3] G. G. Stokes. "J." , 1890, The New Yale Book of Quotations.

[4] A. Balakrishnan,et al. Methods of optimization , 1988 .

[5] R. Stern,et al. Gastric emptying and gastric myoelectrical activity in patients with diabetic gastroparesis: effect of long-term domperidone treatment. , 1989, The American journal of gastroenterology.

[6] Russell C. Eberhart,et al. Neural network PC tools , 1990 .

[7] Roger Fletcher,et al. Practical methods of optimization; (2nd ed.) , 1987 .

[8] Vladimir N. Vapnik,et al. The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[9] Bernhard Schölkopf,et al. Comparing support vector machines with Gaussian kernels to radial basis function classifiers , 1997, IEEE Trans. Signal Process..

[10] R. Fletcher. Practical Methods of Optimization , 1988 .

[11] R S Fisher,et al. Gastroduodenal motility and dysmotility: an update on techniques available for evaluation. , 1995, The American journal of gastroenterology.