An IFS-based similarity measure to index electroencephalograms

Ghita Berrada; Joshua Zhexue Huang; Ander de Keijzer; Longbing Cao; Jaideep Srivastava

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

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 conference › Paper

2 Citations (Scopus)

134 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 language	English
Pages	457-468
Number of pages	12
DOIs	https://doi.org/10.1007/978-3-642-20847-8_38
Publication status	Published - May 2011
Event	15th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2011 - Shenzhen, China, Shenzhen, China Duration: 24 May 2011 → 27 May 2011 Conference number: 15

Conference

Conference	15th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2011
Abbreviated title	PAKDD 2011
Country/Territory	China
City	Shenzhen
Period	24/05/11 → 27/05/11
Other	24-27 May 2011

Keywords

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

Access to Document

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

pakdd_fractal

Cite this

@conference{bd41f259ba8c4e328301cec7c70041be,

title = "An IFS-based similarity measure to index electroencephalograms",

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.",

keywords = "iterated function systems (IFS), indexing, Clustering, IR-89234, EWI-24411, electroencephalograms (EEG)",

author = "Ghita Berrada and {Zhexue Huang}, Joshua and {de Keijzer}, Ander and Longbing Cao and Jaideep Srivastava",

year = "2011",

month = may,

doi = "10.1007/978-3-642-20847-8_38",

language = "English",

pages = "457--468",

note = "15th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2011, PAKDD 2011 ; Conference date: 24-05-2011 Through 27-05-2011",

}

TY - CONF

T1 - An IFS-based similarity measure to index electroencephalograms

AU - Berrada, Ghita

AU - de Keijzer, Ander

A2 - Zhexue Huang, Joshua

A2 - Cao, Longbing

A2 - Srivastava, Jaideep

N1 - Conference code: 15

PY - 2011/5

Y1 - 2011/5

N2 - 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.

AB - 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.

KW - iterated function systems (IFS)

KW - indexing

KW - Clustering

KW - IR-89234

KW - EWI-24411

KW - electroencephalograms (EEG)

U2 - 10.1007/978-3-642-20847-8_38

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

M3 - Paper

SP - 457

EP - 468

T2 - 15th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2011

Y2 - 24 May 2011 through 27 May 2011

ER -

An IFS-based similarity measure to index electroencephalograms

Abstract

Conference

Keywords

Access to Document

Fingerprint

Cite this