An EEG spike detection algorithm using artificial neural network with multi-channel correlation
Resource
Engineering in Medicine and Biology Society, 1998. Proceedings of the 20th Annual International Conference of the IEEE
Journal
20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 1998
Pages
-
Date Issued
1998-11
Date
1998-11
Author(s)
DOI
N/A
Abstract
An automatic spike detection algorithm for classification of multi-channel EEG signals based on artificial neural network is presented. Radial basis function (RBF) neural network was chosen for single channel recognition, with model optimization using receiver operating characteristics analysis. Waveform simplification was employed for high noise immunity. Feature extraction with as few as three parameters was used as preparation for the inputs to the neural network. Identification of multi-channel geometric correlation was performed to further lower the false-positive rate by using an incidence matrix. Threshold value for spike classification was chosen for simultaneous maximization of detection sensitivity and selectivity. Evaluation with visual analysis in this preliminary study showed a 83% sensitivity using 16-channel continuous EEG records of four patients, while a high false positive rate was found, which was believed to arise from the extensive and exhaustive visual analysis process. The computation time required for spike detection was significantly less than that needed for online display of the signals on the monitor. We believe that the algorithm proposed in this study is robust and that the simple structure of RBF neural network yields high potential for real-time implementation.
Type
journal article
File(s)![Thumbnail Image]()
Loading...
Name
00747014.pdf
Size
382.12 KB
Format
Adobe PDF
Checksum
(MD5):1b0855c69317647fe32b30bb87cfbc4f