Abstract

The long-term monitoring of electroencephalogram (EEG) produces a large amount of data that needs to be analyzed We have applied the method of Kohonen's self-organizing feature mapping for classification of epileptic EEG signals to reduce the time needed for the analysis. The results of our experiments show that this new method is suitable for automatic classification of various normal and epileptic signals. The developed system is more sensitive to epileptic seizures than currently available systems.

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 call

Disclaimer: 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.