An IFS-based similarity measure to index electroencephalograms

Ghita Berrada; Ander De Keijzer

doi:10.1007/978-3-642-20847-8_38

An IFS-based similarity measure to index electroencephalograms

Ghita Berrada^*, Ander De Keijzer

^*Corresponding author for this work

Population Health Sciences

University of Twente

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

2 Citations (Scopus)

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 language	English
Title of host publication	Advances in Knowledge Discovery and Data Mining - 15th Pacific-Asia Conference, PAKDD 2011, Proceedings
Publisher	Springer Verlag
Pages	457-468
Number of pages	12
Edition	PART 2
ISBN (Print)	9783642208461
DOIs	https://doi.org/10.1007/978-3-642-20847-8_38
Publication status	Published - 2011

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 2
Volume	6635 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Keywords

clustering
electroencephalograms (EEG)
indexing
iterated function systems (IFS)

Access to Document

10.1007/978-3-642-20847-8_38

Cite this

Berrada, G., & De Keijzer, A. (2011). An IFS-based similarity measure to index electroencephalograms. In Advances in Knowledge Discovery and Data Mining - 15th Pacific-Asia Conference, PAKDD 2011, Proceedings (PART 2 ed., pp. 457-468). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6635 LNAI, No. PART 2). Springer Verlag. https://doi.org/10.1007/978-3-642-20847-8_38

Berrada, Ghita ; De Keijzer, Ander. / An IFS-based similarity measure to index electroencephalograms. Advances in Knowledge Discovery and Data Mining - 15th Pacific-Asia Conference, PAKDD 2011, Proceedings. PART 2. ed. Springer Verlag, 2011. pp. 457-468 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2).

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Berrada, G & De Keijzer, A 2011, An IFS-based similarity measure to index electroencephalograms. in Advances in Knowledge Discovery and Data Mining - 15th Pacific-Asia Conference, PAKDD 2011, Proceedings. PART 2 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 2, vol. 6635 LNAI, Springer Verlag, pp. 457-468. https://doi.org/10.1007/978-3-642-20847-8_38

An IFS-based similarity measure to index electroencephalograms. / Berrada, Ghita; De Keijzer, Ander.
Advances in Knowledge Discovery and Data Mining - 15th Pacific-Asia Conference, PAKDD 2011, Proceedings. PART 2. ed. Springer Verlag, 2011. p. 457-468 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6635 LNAI, No. PART 2).

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

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Berrada G, De Keijzer A. An IFS-based similarity measure to index electroencephalograms. In Advances in Knowledge Discovery and Data Mining - 15th Pacific-Asia Conference, PAKDD 2011, Proceedings. PART 2 ed. Springer Verlag. 2011. p. 457-468. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2). doi: 10.1007/978-3-642-20847-8_38

An IFS-based similarity measure to index electroencephalograms

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