Rough set-based classification of EEG-signals to detect intraoperative awareness: Comparison of fuzzy and crisp discretization of real value attributes

Michael Ningler, Gudrun Stockmanns, Gerhard Schneider, Oliver Dressler, Eberhard F. Kochs

Publikation: Beitrag in FachzeitschriftKonferenzartikelBegutachtung

11 Zitate (Scopus)

Abstract

Automated classification of calculated EEG parameters has been shown to be a promising method for detection of intraoperative awareness. In the present study, rough set-based methods were employed to generate classification rules. For these methods, discrete attributes are required. We compared a crisp and a fuzzy discretization of the real parameter values. Fuzzy discretization transforms one real attribute value to several discrete values. By combining the different (discrete) values of all attributes, several sub-objects were produced from a single original object. Rule generation from a training set of objects and classification of a test set provided good classification rates of approximately 90% for both crisp and fuzzy discretization. Fuzzy discretization resulted in a simpler and smaller rule set than crisp discretization. Therefore, the simplicity of the resulting classifier justifies the higher computational effort caused by fuzzy discretization.

OriginalspracheEnglisch
Seiten (von - bis)825-834
Seitenumfang10
FachzeitschriftLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Jahrgang3066
DOIs
PublikationsstatusVeröffentlicht - 2004
Veranstaltung4th International Conference, RSCTC 2004 - Uppsala, Schweden
Dauer: 1 Juni 20045 Juni 2004

Fingerprint

Untersuchen Sie die Forschungsthemen von „Rough set-based classification of EEG-signals to detect intraoperative awareness: Comparison of fuzzy and crisp discretization of real value attributes“. Zusammen bilden sie einen einzigartigen Fingerprint.

Dieses zitieren