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dc.contributor |
Universitat de Vic. Escola Politècnica Superior |
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dc.contributor |
Universitat de Vic. Grup de Recerca en Tecnologies Digitals |
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dc.contributor |
International Conference on Bio-inspired Systems and Signal Proceesing (3a: 2010: València) |
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dc.contributor |
BIOSIGNALS 2010 |
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dc.contributor.author |
Solé-Casals, Jordi |
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dc.contributor.author |
Vialatte, François B. |
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dc.contributor.author |
Pantel, J. |
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dc.contributor.author |
Prvulovic, D. |
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dc.contributor.author |
Haenschel, C. |
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dc.contributor.author |
Cichocki, Andrej |
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dc.date.accessioned |
2014-04-07T12:06:49Z |
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dc.date.available |
2014-04-07T12:06:49Z |
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dc.date.created |
2010 |
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dc.date.issued |
2010 |
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dc.identifier.citation |
Sole-Casals, J., Vialatte, F., Pantel, J., Prvulovic, D., Haenschel, C., & Cichocki, A. (2010). ICA cleaning procedure for EEG signals analysis: Application to Alzheimer's disease detection. , València 485-490. |
ca_ES |
dc.identifier.uri |
http://hdl.handle.net/10854/2857 |
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dc.description.abstract |
To develop systems in order to detect Alzheimer’s disease we want to use EEG signals. Available database
is raw, so the first step must be to clean signals properly. We propose a new way of ICA cleaning on a
database recorded from patients with Alzheimer's disease (mildAD, early stage). Two researchers visually
inspected all the signals (EEG channels), and each recording's least corrupted (artefact-clean) continuous 20
sec interval were chosen for the analysis. Each trial was then decomposed using ICA. Sources were ordered
using a kurtosis measure, and the researchers cleared up to seven sources per trial corresponding to artefacts
(eye movements, EMG corruption, EKG, etc), using three criteria: (i) Isolated source on the scalp (only a
few electrodes contribute to the source), (ii) Abnormal wave shape (drifts, eye blinks, sharp waves, etc.),
(iii) Source of abnormally high amplitude (�100 �V). We then evaluated the outcome of this cleaning by
means of the classification of patients using multilayer perceptron neural networks. Results are very
satisfactory and performance is increased from 50.9% to 73.1% correctly classified data using ICA cleaning
procedure. |
en |
dc.format |
application/pdf |
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dc.format.extent |
6 p. |
ca_ES |
dc.language.iso |
eng |
ca_ES |
dc.rights |
Tots els drets reservats |
ca_ES |
dc.subject.other |
Alzheimer, Malaltia d' |
ca_ES |
dc.title |
ICA Cleaning procedure for EEG signals analysis: application to Alzheimer's disease detection |
en |
dc.type |
info:eu-repo/semantics/conferenceObject |
ca_ES |
dc.rights.accessRights |
info:eu-repo/semantics/openAccess |
ca_ES |
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