Detection of epileptiform spikes in the EEG using a patient-independent neural network

Kerry Wilson, W. Robert S. Webber, Ronald P. Lesser, Robert S. Fisher, Russell C. Eberhart, Roy W. Dobbins

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

Abstract

An offline neural network that successfully detects spikes when trained on multiple patients selected from a database of electroencephalogram (EEG) records with spikes marked by experienced electroencephalographers has been developed. This spike detector uses a simple threshold detector to identify potential spikes that appear on four-channel bipolar chains within the montage, and then passes waveform parameters to a three-layer neural network for second-level detection. Results obtained for the neural network with output thresholds arbitrarily set of 0.5 have yielded sensitivities averaging 74% and selectivities averaging 54%. While the selectivities for these trials were only fair, it is noted that substantial improvements could be achieved by raising the output thresholds.

Original languageEnglish (US)
Title of host publicationProc 4 Annu Symp Comput Based Med Syst
PublisherPubl by IEEE
Pages264-271
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

    Wilson, K., Webber, W. R. S., Lesser, R. P., Fisher, R. S., Eberhart, R. C., & Dobbins, R. W. (1991). Detection of epileptiform spikes in the EEG using a patient-independent neural network. In Proc 4 Annu Symp Comput Based Med Syst (pp. 264-271). (Proc 4 Annu Symp Comput Based Med Syst). Publ by IEEE.