Global Approach to Multichannel Electroencephalogram Analysis for Diagnosis and Clinical Evaluation in Mild Alzheimer’s Disease

Yoshimura, Masafumi; Isotani, Toshiaki; Yagyu, Takami; Irisawa, Satoshi; Yoshida, Terufumi; Sugiyama, Masao; Minami, Toyohiko; Sugimoto, Tatsuya; Nobuhara, Kenji; Okugawa, Gaku; Kinoshita, Toshihiko

doi:10.1159/000076724

Cited by 25 publications

(16 citation statements)

References 22 publications

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“…We report here a low (uncorrected) p-value for the indices N k (p = 0.029; uncorrected) and S k (p = 0.019; uncorrected), and the S-estimator (p = 0.042; uncorrected); however, after post-correction, those effects are not statistically significant. Omega complexity was found to be greater in patients with mild AD, which corresponds to decreased synchronization (Yoshimura et al, 2004). We also observed that effect in our data, but it was weak (p = 0.079; uncorrected).…”

Section: Discussionsupporting

confidence: 72%

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A comparative study of synchrony measures for the early diagnosis of Alzheimer's disease based on EEG

Dauwels

Vialatte

Musha³

et al. 2010

NeuroImage

371

384

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It is well known that EEG signals of Alzheimer's disease (AD) patients are generally less synchronous than in age-matched control subjects. However, this effect is not always easily detectable. This is especially the case for patients in the pre-symptomatic phase, commonly referred to as Mild Cognitive Impairment (MCI), during which neuronal degeneration is occurring prior to the clinical symptoms appearance. In this paper, various synchrony measures are studied in the context of AD diagnosis, including the correlation coefficient, mean-square and phase coherence, Granger causality, phase synchrony indices, information-theoretic divergence measures, state space based measures, and the recently proposed stochastic event synchrony measures. Experiments with EEG data show that many of those measures are strongly correlated (or anti-correlated) with the correlation coefficient, and hence, provide little complementary information about EEG synchrony. Measures that are only weakly correlated with the correlation coefficient include the phase synchrony indices, Granger causality measures, and stochastic-event synchrony measures. In addition, those three families of synchrony measures are mutually uncorrelated, and therefore, they each seem to capture a specific kind of interdependence. For the data set at hand, only two synchrony measures are able to convincingly distinguish AD patients from age-matched control patients, i.e., Granger causality (in particular, full-frequency directed transfer function) and stochastic event synchrony. Those two measures are used as features to distinguish MCI patients from age-matched control subjects, yielding a leave-one-out classification rate of 83%. The classification performance may be further improved by adding complementary features from EEG; this approach may eventually lead to a reliable EEG-based diagnostic tool for MCI and AD.

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Section: Discussionsupporting

confidence: 72%

“…Omega complexity Ω is a measure derived from principal component analysis (PCA) ) (see (Yoshimura et al, 2004) for an application to EEG). Suppose that we are given n signals X 1 (k), X 2 (k), .…”

Section: Omega Complexity and S-estimatormentioning

confidence: 99%

A comparative study of synchrony measures for the early diagnosis of Alzheimer's disease based on EEG

Dauwels

Vialatte

Musha³

et al. 2010

NeuroImage

371

384

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“…An increase in theta and delta activity and a decrease in alpha and beta activity are commonly observed (Coben et al, 1983(Coben et al, , 1985Giaquinto and Nolfe, 1986;Brenner et al, 1986;Pijnenburg et al, 2004). Furthermore, these abnormalities are correlated with the severity of the disease (Hughes et al, 1989;Kowalski et al, 2001), and a significant correlation has been reported between the generalized frequency value and MMSE scores (Yoshimura et al, 2004). Thus, over the past three decades, the EEG has been found to be a useful tool for detecting, through the extraction of certain features, patients affected by AD and cognitive impairment (Babiloni et al, 2004;Huang et al, 2000;Dierks et al, 1993;Jonkman, 1997;Jeong, 2004;Jung et al, 2007).…”

Section: Functional Neurology 2014; 29(1): 57-65mentioning

confidence: 96%

Elman neural network for the early identification of cognitive impairment in Alzheimer�s disease

Bertè¹,

Lamponi²,

Calabrò

et al. 2014

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Early detection of dementia can be useful to delay progression of the disease and to raise awareness of the condition. Alterations in temporal and spatial EEG markers have been found in patients with Alzheimer's disease (AD) and mild cognitive impairment (MCI). Herein, we propose an automatic recognition method of cognitive impairment evaluation based on EEG analysis using an artificial neural network (ANN) combined with a genetic algorithm (GA). The EEGs of 43 AD and MCI patients (aged between 62 and 88 years) were recorded, analyzed and correlated with their MMSE scores. Quantitative EEGs were calculated using discrete wavelet transform. The data obtained were analyzed by the means of the combined use of ANN and GA to determine the degree of cognitive impairment. The good recognition rate of ANN fed with these inputs suggests that the combined GA/ANN approach may be useful for early detection of AD and could be a valuable tool to support physicians in clinical practice. KEY WORDS: artificial neural network, dementia, EEG, genetic algorithm, MMSE.

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“…The index S was found to be significantly lower in AD patients [98]. In a related study [110], the index H was significantly lower in MCI and AD patients, however, no such effect was observed for S. Omega complexity was found to be increased in patients with mild AD, which corresponds to decreased synchronization [146]; that effect was also observed in MCI patients [97], but it was non-significant.…”

Section: State Space Synchronymentioning

confidence: 99%

Diagnosis of Alzheimers Disease from EEG Signals: Where Are We Standing?

Dauwels¹,

Vialatte²,

Cichocki³

2010

CAR

389

188

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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.

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Global Approach to Multichannel Electroencephalogram Analysis for Diagnosis and Clinical Evaluation in Mild Alzheimer’s Disease

Cited by 25 publications

References 22 publications

A comparative study of synchrony measures for the early diagnosis of Alzheimer's disease based on EEG

A comparative study of synchrony measures for the early diagnosis of Alzheimer's disease based on EEG

Elman neural network for the early identification of cognitive impairment in Alzheimer�s disease

Diagnosis of Alzheimers Disease from EEG Signals: Where Are We Standing?

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