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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. |
