Detection of the EEG K-complex wave with neural networks

Isaac N. Bankman, Vincent G. Sigillito, Robert A. Wise, Philip L. Smith

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

Abstract

The K-complex detection task is approached by first extracting morphological features that quantify the visual recognition criteria used for both acceptance and rejection of candidate waveforms. The features are based on amplitude and duration measurements. These features are used as the inputs of multivariate discrimination methods. The performance of Fisher's linear discriminant with multilayer feedforward neural networks (MLFNs) in discriminating the K-complex and background EEG is compared. The results show that the use of the MLFN on feature information can provide a reliable K-complex detection with significantly better performance than that of the linear discriminant. This difference in performance can be seen on the receiver operating characteristics curves that show the true positive against the false positives.

Original languageEnglish (US)
Title of host publicationProc 4 Annu Symp Comput Based Med Syst
PublisherPubl by IEEE
Pages280-287
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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