Multilevel neural network system for EEG spike detection

Ozcan Ozdamar, Ilker Yaylali, Prasanna Jayaker, Carlos N. Lopez

Research output: Chapter in Book/Report/Conference proceedingConference contribution

30 Scopus citations

Abstract

The design and evaluation of an artificial neural network system for the detection of epileptogenic spikes is described. The system is composed of smaller neural network modules which are trained individually and organized in two levels. The first-level modules are trained to recognize candidate spikes in single referential electroencephalogram (EEG) channels. Original digitized data with a running window of 100 ms provided the input for the first-level modules. A second-level module is used for the spatial integration of 16 first-level modules. The system was trained and tested using clinical EEG data interpreted by four expert electroencephalographers. The results show that spikes can be recognized directly from unprocessed EEG and a second-level neural network can integrate spatial EEG information and eliminate false detections.

Original languageEnglish (US)
Title of host publicationProc 4 Annu Symp Comput Based Med Syst
PublisherPubl by IEEE
Pages272-279
Number of pages8
ISBN (Print)0818621648
StatePublished - Jan 1 1991
EventProceedings of the 4th Annual Symposium on Computer-Based Medical Systems -
Duration: May 12 1991May 14 1991

Publication series

NameProc 4 Annu Symp Comput Based Med Syst

Other

OtherProceedings of the 4th Annual Symposium on Computer-Based Medical Systems
Period5/12/915/14/91

ASJC Scopus subject areas

  • Engineering(all)

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  • Cite this

    Ozdamar, O., Yaylali, I., Jayaker, P., & Lopez, C. N. (1991). Multilevel neural network system for EEG spike detection. In Proc 4 Annu Symp Comput Based Med Syst (pp. 272-279). (Proc 4 Annu Symp Comput Based Med Syst). Publ by IEEE.