Abstract
The feasibility of using a multi-layer perceptron and Elman's recurrent network for the detection of specific waveforms (K-complexes) in electroencephalograms (EEGs), regardless of their location in the signal segment, is explored. Experiments with simulated and actual EEG data were performed. In case of the perceptron, the input consisted of the magnitude and/or phase values obtained from 10-s signal intervals, whereas the recurrent net operated on the digitized data samples directly. It was found that both nets performed well on the simulated data, but not on the actual EEG data. The reasons for the failure of both nets are discussed.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
