ICA cleaning procedure for EEG signals analysis: Application to Alzheimer's disease detection
| Autor: | Solé-Casals, J., Francois Vialatte, Pantel, J., Prvulovic, D., Haenschel, C., Cichocki, A. |
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| Přispěvatelé: | Universitat de Vic. Escola Politècnica Superior, Universitat de Vic. Grup de Recerca en Tecnologies Digitals, International Conference on Bio-inspired Systems and Signal Proceesing (3a: 2010: València), BIOSIGNALS 2010 |
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| Zdroj: | Scopus-Elsevier Recercat. Dipósit de la Recerca de Catalunya instname RIUVic. Repositorio Institucional de la Universidad de Vic |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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