Preprocessing the EEG of Alzheimer’s patients to automatically remove artifacts

Alzheimer’s disease (AD) is a neurological degenerative disorder that causes the impairment of memory, behaviour and cognitive abilities. AD is considered a cortical disease because it causes the loss of functional connections between the cortical regions. Electroencephalography (EEG) consists in recording, noninvasively, the electrical potentials produced by neuronal activity. EEG is used in the evaluation of AD patients because they show peculiar EEG features. The EEG traces of AD patients usually exhibit a shift of the power spectrum to lower frequencies as well as reduced coherence between the cortical areas. This is the reason why AD is defined as “disconnection disorder”. However, the correct interpretation of the EEG can be very challenging because of the presence of “artifacts”, undesired signals that overlap to the EEG signals generated by the brain. Removing artifacts is therefore crucial in EEG processing. Recently, the author contributed to develop an automatic EEG artifact rejection methodology called Enhanced Automatic Wavelet Independent Component Analysis (EAWICA) which achieved very good performance on both simulated and real EEG from healthy subjects (controls). The aim of the present paper is to test EAWICA on real EEG from AD patients. According to the expert physician’s feedback, EAWICA efficiently removed the artifacts while saving the diagnostic information embedded in the EEG and not affecting the segments that were originally artifact free.