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research
Spike pattern recognition by supervised classification in low dimensional embedding space
Authors
A Globerson
AA Dingle
+43 more
AJ Gabor
AT Tzallas
B Ramabhadran
BL Davey
C Kurth
CJ James
DK Agrafiotis
EI Zacharaki
EI Zacharaki
F Sartoretto
FI Argoud
G Fischer
H Goelz
H Witte
HS Liu
HS Park
International Federation of Societies for Clinical Neurophysiology
IT Jolliffe
J Gotman
J Zhang
JJ Halford
KJ Staley
KP Indiradevi
L Senhadji
M Adjouadi
M Adjouadi
M Belkin
M Feucht
M Latka
M Lucia De
N Acir
O Ozdamar
P Hese Van
RR Coifman
SB Wilson
SB Wilson
T Sugi
T Zhang
TP Exarchos
WE Hostetler
WR Webber
X He
ZH Inan
Publication date
1 January 2016
Publisher
'Springer Science and Business Media LLC'
Doi
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on
PubMed
Abstract
© The Author(s) 2016. This article is published with open access at Springerlink.com under the terms of the Creative Commons Attribution License 4.0, (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.Epileptiform discharges in interictal electroencephalography (EEG) form the mainstay of epilepsy diagnosis and localization of seizure onset. Visual analysis is rater-dependent and time consuming, especially for long-term recordings, while computerized methods can provide efficiency in reviewing long EEG recordings. This paper presents a machine learning approach for automated detection of epileptiform discharges (spikes). The proposed method first detects spike patterns by calculating similarity to a coarse shape model of a spike waveform and then refines the results by identifying subtle differences between actual spikes and false detections. Pattern classification is performed using support vector machines in a low dimensional space on which the original waveforms are embedded by locality preserving projections. The automatic detection results are compared to experts’ manual annotations (101 spikes) on a whole-night sleep EEG recording. The high sensitivity (97 %) and the low false positive rate (0.1 min−1), calculated by intra-patient cross-validation, highlight the potential of the method for automated interictal EEG assessment.Peer reviewedFinal Published versio
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