Title :
Automatic detection of epileptiform discharges in EEG using a back-propagation network
Author :
Xie, Feng ; Yan, Zhuangzhi ; Liu, Shupeng
Author_Institution :
Dept. of Biomed. Eng., Shanghai Univ., China
Abstract :
This paper presents an automatic approach to detect epileptiform discharges (ED) in electroencephalogram (EEG). On the algorithm we utilized back-propagation artificial neural network (BPN) to detect ED. We train BPN respectively for each patient and induce parameter k to determine a threshold value. The result shows that the algorithm can determine presence or absence of ED automatically, and decrease the false determination in current automated approaches as well.
Keywords :
backpropagation; electroencephalography; medical signal processing; neural nets; pattern classification; signal classification; EEG; automatic detection; backpropagation neural network; cross recognition method; epileptiform discharges; output peak value distribution curve; power function curve; threshold value; Artificial neural networks; Biomedical engineering; Detection algorithms; Electroencephalography; Electronic mail; Epilepsy; Frequency; Intelligent networks; Pattern recognition; Scalp;
Conference_Titel :
Engineering in Medicine and Biology Society, 2001. Proceedings of the 23rd Annual International Conference of the IEEE
Print_ISBN :
0-7803-7211-5
DOI :
10.1109/IEMBS.2001.1020565