Towards automated electroencephalography-based Alzheimer's disease diagnosis using portable low-density devices. (March 2017) - Cassani, Raymundo; Falk, Tiago H.; Fraga, Francisco J.; Cecchi, Marco; Moore, Dennis K.; Anghinah, Renato
Graphical abstract: Highlights: Electroencephalography, a non-invasive technique to study Alzheimer's disease. Enhancement of low-density EEG signals with automated artifact removal. EEG-based Alzheimer's disease diagnosis, promising tool in developing countries. Leave-One-Subject-Out cross-validation to reduce optimistic bias in classification. Abstract: Today, Alzheimer's disease (AD) diagnosis is carried out using subjective mental status examinations assisted in research by scarce and expensive neuroimaging scans and invasive laboratory tests; all of which render the diagnosis time-consuming, geographically confined and costly. Driven by these limitations, quantitative analysis of electroencephalography (EEG) has been proposed as a non-invasive and more convenient technique to study AD. Published works on EEG-based AD diagnosis typically share two main characteristics: EEG is manually selected by experienced clinicians to discard artefacts that affect AD diagnosis, and reliance on EEG devices with 20 or more electrodes. Recent work, however, has suggested promising results by using automated artefact removal (AAR) algorithms combined with medium-density EEG setups. Over the last couple of years, however, low-density, portable EEG devices have emerged, thus opening the doors for low-cost AD diagnosis in low-income countries and remote regions, such as the Canadian Arctic. Unfortunately, the performance of automated diagnostic solutions based on low-density portable devices is still unknown. The work presented here aims to fill this gap. We propose an automated EEG-based AD diagnosis system based on AAR and a low-density (7-channel) EEG setup. EEG data was acquired during resting-awake protocol from control and AD participants. After AAR, common EEG features, spectral power and coherence, are computed along with the recently proposed amplitude-modulation features. The obtained features are used for training and testing of the proposed diagnosis system. We report and discuss the results obtained with such system and compare the obtained performance with results published in the literature using higher-density EEG layouts.