TOWARDS AN EEG-BASED BIOMARKER FOR ALZHEIMER'S DISEASE: IMPROVING AMPLITUDE MODULATION ANALYSIS FEATURES

Abstract

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.