Nonlinear analysis of electroencephalogram and magnetoencephalogram recordings in patients with Alzheimer's disease

Abstract

The aim of the present study is to show the usefulness of nonlinear methods to analyse the electroencephalogram (EEG) and magnetoencephalogram (MEG) in patients with Alzheimer's disease (AD). The following nonlinear methods have been applied to study the EEG and MEG background activity in AD patients and control subjects: approximate entropy, sample entropy, multiscale entropy, auto-mutual information and Lempel-Ziv complexity. We discuss why these nonlinear methods are appropriate to analyse the EEG and MEG. Furthermore, the performance of all these methods has been compared when applied to the same databases of EEG and MEG recordings. Our results show that EEG and MEG background activities in AD patients are less complex and more regular than in healthy control subjects. In line with previous studies, our work suggests that nonlinear analysis techniques could be useful in AD diagnosis.

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

“…1 components. This finding agrees with the fact that AD has been related to spectral abnormalities and a decrease in complexity and irregularity of the electromagnetic brain activity [3][4][5]8]. …”

“…MEGs from those of healthy elderly subjects [5,7,8,14,26,27]. Moreover, since two of them are spectral features (MF and SpecEn) and the other two (LZC and SampEn) are non-linear analysis methods, the usefulness of the BSS and component selection procedure could be tested with both types of techniques.…”

“…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].…”

“…Due to the fact that AD affects the brain cortex and that the EEG and MEG reflect brain cortical activity, the usefulness of these recordings to help in the diagnosis of this dementia has been extensively researched in the last decades [3,4]. EEG and MEG have been analysed with several signal processing techniques to gain insight into AD [3][4][5]. For instance, spectral features have been used to quantify the abnormalities in the spectra of AD patients' EEGs and MEGs [3,[6][7][8].…”

On the Early Diagnosis of Alzheimer’s Disease from EEG Signals: A Mini-Review

“…The following complexity measures have been used to quantify this reduction in EEG complexity: approximate entropy [6], auto mutual information [6], sample entropy [6,8], multiscale entropy [6], Lempel-Ziv complexity [6], and fractal dimension [7].…”