ICA Cleaning procedure for EEG signals analysis: application to Alzheimer's disease detection

摘要

To develop systems in order to detect Alzheimer’s disease we want to use EEG signals. Available databaseis raw, so the first step must be to clean signals properly. We propose a new way of ICA cleaning on adatabase recorded from patients with Alzheimer's disease (mildAD, early stage). Two researchers visuallyinspected all the signals (EEG channels), and each recording's least corrupted (artefact-clean) continuous 20sec interval were chosen for the analysis. Each trial was then decomposed using ICA. Sources were orderedusing 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 afew 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 bymeans of the classification of patients using multilayer perceptron neural networks. Results are verysatisfactory and performance is increased from 50.9% to 73.1% correctly classified data using ICA cleaningprocedure.