Diagnosis of Alzheimer's Disease from EEG Signals: Where Are We Standing?

Abstract

This paper reviews recent progress in the diagnosis of Alzheimer's disease (AD) from electroencephalograms (EEG). Three major effects of AD on EEG have been observed: slowing of the EEG, reduced complexity of the EEG signals, and perturbations in EEG synchrony. In recent years, a variety of sophisticated computational approaches has been proposed to detect those subtle perturbations in the EEG of AD patients. The paper first describes methods that try to detect slowing of the EEG. Next the paper deals with several measures for EEG complexity, and explains how those measures have been used to study fluctuations in EEG complexity in AD patients. Then various measures of EEG synchrony are considered in the context of AD diagnosis. Also the issue of EEG pre-processing is briefly addressed. Before one can analyze EEG, it is necessary to remove artifacts due to for example head and eye movement or interference from electronic equipment. Pre-processing of EEG has in recent years received much attention. In this paper, several state-of-the-art pre-processing techniques are outlined, for example, based on blind source separation and other non-linear filtering paradigms. In addition, the paper outlines opportunities and limitations of computational approaches for diagnosing AD based on EEG. At last, future challenges and open problems are discussed.

Power spectral density and coherence analysis of Alzheimer’s EEG

“…Our findings are in accordance with other studies showing spectral ''slowing'' in AD (Czigler et al 2008;Dauwels et al 2010aDauwels et al , b, 2011van der Hiele et al 2007). For instance, Jeong (2004) have observed a slowing of the resting EEG in AD patients, reflected by a higher power in the theta frequency band.…”

“…It is generally accepted that there are alterations of EEG rhythms in the AD brain: (1) EEG slowing; that is, a shift of the power spectrum toward the lower frequency bands (delta and theta band) along with a decrease of oscillations in the higher frequency bands (alpha and beta); (2) decreased complexity, which is strongly related to the slowing of EEG; and (3) decreased synchrony, from which the functional network can be extracted (Dauwels et al 2010a(Dauwels et al , b, 2011(Dauwels et al , 2013Stam et al 2003Stam et al , 2005Stam et al , 2007. Notably, AD has been described as a disconnection syndrome, where functional interactions in the brain are strongly affected by anatomical abnormalities among different cortical areas (Jelles et al 1999) and altered cholinergic coupling interactions among cortical neurons (Jeong 2004).…”

“…Experimental studies show that it is may be caused by the degeneration of synapses and death of neurons in the brain regions, such as hippocampus, entorhinal cortex, neocortex. It usually results in a loss in cognition, memory, judgment, even language and functional skills (Dauwels et al 2010a(Dauwels et al , b, 2011Mattson 2004). It is asserted that a definite diagnosis is only possible by necropsy (Dauwels et al 2010a, b).…”

“…Investigation of neuropsychiatric disorder using neuroscientific approaches, such as functional magnetic resonance imaging (fMRI) (Chang and Glover 2010;Vemuri et al 2012;Zhou et al 2010), magnetoencephalography (MEG) (Stam et al 2006;Zhang et al 2014), electroencephalography (EEG) (Dauwels et al 2010a(Dauwels et al , b, 2013Jelles et al 2008;Vialatte et al 2012) and so on, attracts more interests. As a noninvasive, simple, and relatively low cost approach, resting-state EEG indirectly measures brain neural electric activity from the scalp of the head.…”

Event-Related Brain Potentials in the Study of Inhibition: Cognitive Control, Source Localization and Age-Related Modulations

“…It is a voltage fluctuation, which is derived from the ongoing electroencephalogram (EEG), that is time-locked to a specific event (Kuperberg 2004). These voltage fluctuations are represented in the ERP waveform as a series of positive and negative peaks that vary in amplitude and latency (Dauwels et al 2010). The amplitude can be measured as the difference between the maximum peak of the ERP waveform over a period of time and the mean baseline voltage (which occurs prior to the stimulus) (Polich 2007).…”