Feature-Based Detection of the K-Complex Wave in the Human Electroencephalogram Using Neural Networks

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

Research output: Contribution to journalArticle

Abstract

The main difficulties in reliable automated detection of the K-complex wave in EEG are its close similarity to other waves and the lack of specific characterization criteria. We present a feature-based detection approach using neural networks that provides good agreement with visual K-complex recognition: a sensitivity of 90% is obtained with about 8% false positives. The respective contribution of the features and that of the neural network is demonstrated by comparing the results to those obtained with i) raw EEG data presented to neural networks, and ii) features presented to Fisher's linear discriminant.

Original languageEnglish (US)
Pages (from-to)1305-1310
Number of pages6
JournalIEEE Transactions on Biomedical Engineering
Volume39
Issue number12
DOIs
StatePublished - Dec 1992

ASJC Scopus subject areas

  • Biomedical Engineering

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