In this paper, an EEG-based biomarker for automated Alzheimer's disease (AD) diagnosis is described, based on extending a recently-proposed "percentage modulation energy" (PME) metric. More specifically, to improve the signal-to-noise ratio of the EEG signal, PME features were averaged over different durations prior to classification. Additionally, two variants of the PME features were developed: the "percentage raw energy" (PRE) and the "percentage envelope energy" (PEE). Experimental results on a dataset of 88 participants (35 controls, 31 with mild-AD and 22 with moderate AD) show that over 98% accuracy can be achieved with a support vector classifier when discriminating between healthy and mild AD patients, thus significantly outperforming the original PME biomarker. Moreover, the proposed system can achieve over 94% accuracy when discriminating between mild and moderate AD, thus opening doors for very early diagnosis.

This is the first study of Alzheimer's awake-EEG reporting the influence of alpha rhythm on epoch selection, where our results revealed that, contrarily to what was most likely expected, less synchronized EEG epochs (rare alpha scenario) better discriminated mild Alzheimer's than those presenting abundant alpha (dominant alpha scenario). In addition, we find out that epoch selection is a very sensitive issue in qEEG research. Consequently, for Alzheimer's studies dealing with resting state EEG, we propose that epoch selection strategies should always be cautiously designed and thoroughly explained.

In this work we propose a detailed EEG epoch selection method and compare epochs with rare and abundant alpha rhythm (AR) of patients with Alzheimer's disease (AD) and normal controls. Epochs were classified as Dominant Alpha Scenario (DAS) and Rare Alpha Scenario (RAS) according to the AR percentage (energy within the 8-13 Hz bandwidth) in O1, O2 and Oz electrodes. Participants were divided into four groups: 17 DAS controls (N1), 15 DAS mild-AD patients (AD1), 12 RAS controls (N2) and 15 RAS mild-AD patients (AD2). We found out that scenario factor (DAS vs. RAS, two-way ANOVA) is significant over a great amount of electrode-bandwidth situations. Furthermore, one-way ANOVA showed significant differences between RAS AD and RAS controls in much more situations as compared to DAS. This is the first study using AD awake EEG reporting the decisive influence of alpha rhythm on epoch selection, where our results revealed that, contrary to what was initially expected, EEG epochs with poor alpha (RAS) discriminate mild AD much better than those presenting richer alpha content (DAS).