Development and assessment of methods for detecting dementia using the human electroencephalogram

Abstract

This paper makes an outline case for the need for a low-cost, easy to administer method for detecting dementia within the growing at risk population. It proposes two methods for electroencephalogram (EEG) analysis for detecting dementia that could fulfil such a need. The paper describes a fractal dimension-based method for analyzing the EEG waveforms of subjects with dementia and reports on an assessment which demonstrates that an appropriate fractal dimension measure could achieve 67% sensitivity to probable Alzheimer's disease (as suggested by clinical psychometric testing and EEG findings) with a specificity of 99.9%. An alternative method based on the probability density function of the zero-crossing intervals is shown to achieve 78% sensitivity to probable Alzheimer's disease and an estimated sensitivity to probable Vascular (or mixed) dementia of 35% (as suggested by clinical psychometric testing and EEG findings) with a specificity of 99.9%. This compares well with other studies, reported by the American Academy of Neurology, which typically provide a sensitivity of 81% and specificity of 70%. The EEG recordings used to assess these methods included artefacts and had no a priori selection of elements "suitable for analysis." This approach gives a good prediction of the usefulness of the methods, as they would be used in practice. A total of 39 patients (30 probable Alzheimer's Disease, six Vascular Dementia and three mixed dementia) and 42 healthy volunteers were involved in the study. However, although results from the preliminary evaluation of the methods are promising, there is a need for a more extensive study to validate the methods using EEGs from a larger and more varied patient cohorts with neuroimaging results, to exclude other causes and cognitive scores to correlate results with severity of cognitive status.

Medical Big Data: Neurological Diseases Diagnosis Through Medical Data Analysis

“…Numerous studies have been conducted on the CAD system to deal with EEG changes associated with dementia. The researchers developed CAD methods to identify the degree of severity of dementia, and some studies support the possibility for EEG to detect dementia in early stages [46][47][48][49][50]. For example, Henderson et al [51] detected early dementia presence in EEGs with high sensitivity and specificity [46,51].…”

Electrophysiological entropy in younger adults, older controls and older cognitively declined adults

“…For example, a chaos based estimate for the uncertainty of an EEG signal, namely the fractal dimension of the waveform (a measure of self similarity at different scales), was reported in Henderson et al (2006) as being suitable for the identification of subjects with Alzheimer's disease (AD). Watanabe et al (2003) showed that the Lempel-Ziv (LZ) complexity (Ziv and Lempel, 1978), which is a measure of "symbolic complexity", could be successfully applied to EEG waveforms to distinguish between different gross brain states and different patterns of EventRelated Potentials (ERPs).…”

Blind source separation to enhance spectral and non-linear features of magnetoencephalogram recordings. Application to Alzheimer's disease

“…For instance, spectral features have been used to quantify the abnormalities in the spectra of AD patients' EEGs and MEGs [3,[6][7][8]. Additionally, nonlinear analysis methods can provide useful information about the brain dynamics in this dementia [4,5,[8][9][10]. Nevertheless, it is desirable to develop novel strategies to help in AD detection from the analysis of the electromagnetic brain activity [9,11,12].…”

“…Additionally, nonlinear analysis methods can provide useful information about the brain dynamics in this dementia [4,5,[8][9][10]. Nevertheless, it is desirable to develop novel strategies to help in AD detection from the analysis of the electromagnetic brain activity [9,11,12]. Techniques based on spatial filtering can help to achieve this goal, as these algorithms offer additional perspectives to examine EEG and MEG signals [11][12][13][14].…”