TY - GEN
T1 - Noninvasive diagnosis of delayed gastric emptying from cutaneous electrogastrograms using neural networks
AU - Lin, Zhiyue
AU - McCallum, Richard W.
AU - Chen, Jian De Z.
PY - 1997/12/1
Y1 - 1997/12/1
N2 - The currently established gastric emptying test requires the patient to take a radioactive test meal and to stay under a gamma camera for acquiring abdominal images for 2 hours. It is invasive and expensive. Since the electrogastrogram (EGG) is a cutaneous recording of gastric myoelectrical activity which modulates gastric motor activity, we hypothesized that delayed gastric emptying might be predicted from the EGG using a neural network approach. In this study, simultaneous recordings of the EGG and the emptying rate of the stomach by means of the established method were made in 152 patients with suspected gastric motility disorders. A multilayer feedforward neural network approach for the diagnosis of delayed gastric emptying from the noninvasive EGG was developed. Using 5 spectral parameters of the EGG as inputs, a correct classification of 85% was achieved with an optimized three-layer network. This study indicates that the neural network approach is a potentially useful tool for the noninvasive diagnosis of delayed gastric emptying.
AB - The currently established gastric emptying test requires the patient to take a radioactive test meal and to stay under a gamma camera for acquiring abdominal images for 2 hours. It is invasive and expensive. Since the electrogastrogram (EGG) is a cutaneous recording of gastric myoelectrical activity which modulates gastric motor activity, we hypothesized that delayed gastric emptying might be predicted from the EGG using a neural network approach. In this study, simultaneous recordings of the EGG and the emptying rate of the stomach by means of the established method were made in 152 patients with suspected gastric motility disorders. A multilayer feedforward neural network approach for the diagnosis of delayed gastric emptying from the noninvasive EGG was developed. Using 5 spectral parameters of the EGG as inputs, a correct classification of 85% was achieved with an optimized three-layer network. This study indicates that the neural network approach is a potentially useful tool for the noninvasive diagnosis of delayed gastric emptying.
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U2 - 10.1109/ICNN.1997.611638
DO - 10.1109/ICNN.1997.611638
M3 - Conference contribution
AN - SCOPUS:0030714395
SN - 0780341228
SN - 9780780341227
T3 - IEEE International Conference on Neural Networks - Conference Proceedings
SP - 67
EP - 70
BT - 1997 IEEE International Conference on Neural Networks, ICNN 1997
T2 - 1997 IEEE International Conference on Neural Networks, ICNN 1997
Y2 - 9 June 1997 through 12 June 1997
ER -