An IFS-Based Similarity Measure to Index Electroencephalograms

Ghita Berrada, Ander de Keijzer

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

Abstract

EEG is a very useful neurological diagnosis tool, inasmuch as the EEG exam is easy to perform and relatively cheap. However, it generates large amounts of data, not easily interpreted by a clinician. Several methods have been tried to automate the interpretation of EEG recordings. However, their results are hard to compare since they are tested on different datasets. This means a benchmark database of EEG data is required. However, for such a database to be useful, we have to solve the problem of retrieving information from the stored EEGs without having to tag each and every EEG sequence stored in the database (which can be a very time-consuming and error-prone process). In this paper, we present a similarity measure, based on iterated function systems, to index EEGs.
Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining
Subtitle of host publication15th Pacific-Asia Conference, PAKDD 2011, Shenzhen, China, May 24-27, 2011, Proceedings, Part II
EditorsJoshua Zhexue Huang, Longbing Cao, Jaideep Srivastava
Place of PublicationBerlin, Heidelberg
PublisherSpringer
Pages457-468
Number of pages12
ISBN (Electronic)978-3-642-20847-8
ISBN (Print)978-3-642-20846-1
DOIs
Publication statusPublished - 2011

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Berlin Heidelberg
Volume6635
ISSN (Print)0302-9743

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