The University of Southampton
University of Southampton Institutional Repository

Clustering epileptiform discharges with an adaptive subspace self-organizing feature map: a simulation study

Clustering epileptiform discharges with an adaptive subspace self-organizing feature map: a simulation study
Clustering epileptiform discharges with an adaptive subspace self-organizing feature map: a simulation study
We present the results of a study where synthetically generated Epileptiform Discharges (EDs) superimposed on normal background EEG are clustered by means of Kohonen's Self-Organizing Feature Map (SOFM) using a set of basis vectors representing adaptive subspaces in place of the more usual weight vector at each node of the network.

A training set of synthetic EDs is generated using a spherical head model assuming current dipole ED generators. The synthetic EDs are superimposed onto normal background EEG and a preliminary pre-processing stage is used to extract Candidate EDs (CEDs) conshsting of ED and non-ED events. The data is clustered using an adaptive subspace algorithm and the resulting map is calibrated using the labeled synthetic data set.

Preliminary results show that the SOFM is well suited to clustering the pre-processed CEDs, where strong clusters of 'real' EDs are evident. The next step of this research is to further our investigations into the clustering of EDs using real data extracted from the interictal EEG.
0-85296-728-4
238-243
James, C.J.
b3733b1f-a6a1-4c9b-b75c-6191d4142e52
Owe, D.
3988e436-d4e8-4dbe-bc73-59c4ecebedf6
James, C.J.
b3733b1f-a6a1-4c9b-b75c-6191d4142e52
Owe, D.
3988e436-d4e8-4dbe-bc73-59c4ecebedf6

James, C.J. and Owe, D. (2000) Clustering epileptiform discharges with an adaptive subspace self-organizing feature map: a simulation study. IEE Medical Signal and Information Processing Conference (MEDSIP 2000). 04 - 06 Sep 2000. pp. 238-243 .

Record type: Conference or Workshop Item (Paper)

Abstract

We present the results of a study where synthetically generated Epileptiform Discharges (EDs) superimposed on normal background EEG are clustered by means of Kohonen's Self-Organizing Feature Map (SOFM) using a set of basis vectors representing adaptive subspaces in place of the more usual weight vector at each node of the network.

A training set of synthetic EDs is generated using a spherical head model assuming current dipole ED generators. The synthetic EDs are superimposed onto normal background EEG and a preliminary pre-processing stage is used to extract Candidate EDs (CEDs) conshsting of ED and non-ED events. The data is clustered using an adaptive subspace algorithm and the resulting map is calibrated using the labeled synthetic data set.

Preliminary results show that the SOFM is well suited to clustering the pre-processed CEDs, where strong clusters of 'real' EDs are evident. The next step of this research is to further our investigations into the clustering of EDs using real data extracted from the interictal EEG.

Full text not available from this repository.

More information

Published date: 2000
Venue - Dates: IEE Medical Signal and Information Processing Conference (MEDSIP 2000), 2000-09-04 - 2000-09-06

Identifiers

Local EPrints ID: 10804
URI: https://eprints.soton.ac.uk/id/eprint/10804
ISBN: 0-85296-728-4
PURE UUID: 4a133c07-db6a-4be6-a2e0-18ee26dbafcc

Catalogue record

Date deposited: 28 Jun 2005
Last modified: 15 Jul 2019 19:36

Export record

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of https://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×