An IFS-based similarity measure to index electroencephalograms

Ghita Berrada, Joshua Zhexue Huang (Editor), Ander de Keijzer, Longbing Cao (Editor), Jaideep Srivastava (Editor)

Research output: Contribution to conferencePaper

2 Citations (Scopus)
41 Downloads (Pure)

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
Pages457-468
Number of pages12
DOIs
Publication statusPublished - May 2011
Event15th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2011 - Shenzhen, China, Shenzhen, China
Duration: 24 May 201127 May 2011
Conference number: 15

Conference

Conference15th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2011
Abbreviated titlePAKDD 2011
CountryChina
CityShenzhen
Period24/05/1127/05/11
Other24-27 May 2011

Keywords

  • iterated function systems (IFS)
  • indexing
  • Clustering
  • IR-89234
  • EWI-24411
  • electroencephalograms (EEG)

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