The feasibility of using a wavelet transform (WT) as a preprocessor for an automated neural network (ANN)-based EEG spike detection system was confirmed. The study aimed at decreasing the input size to the ANN detector, without decreasing the information content of the signal and degrading the detection performance. Because routine clinical EEG requires recordings for many channels, input size becomes a critical design parameter for real-time multichannel spike detection systems. For a sliding window of 20 points, more than 600 input lines will be necessary for a 32-channel system, which is not easily manageable with current ANN technology.
ASJC Scopus subject areas
- Biomedical Engineering