P04.3 Characterisation of EEG at different stages of Alzheimer’s disease (AD)

Goh, C.; Ifeachor, Emmanuel; Henderson, G.; Latchoumane, Charles; Jeong, Jaeseung; Bigan, Cristin; Besleaga, Mircea; Hudson, N.R.; Capotosto, Paolo; Wimalaratna, Sunil

doi:10.1016/j.clinph.2006.06.220

Cited by 9 publications

(7 citation statements)

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“…The EEG data set has been analyzed in previous studies [9][10][11]; the data was obtained using a strict protocol from Derriford Hospital, Plymouth, U.K., and had been collected using standard hospital practices [10] …”

Section: Eeg Data Setmentioning

confidence: 99%

Diagnosis of Alzheimer's disease from EEG by means of synchrony measures in optimized frequency bands

Gallego-Jutglà

Elgendi

Vialatte

et al. 2012

2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Abstract-Several clinical studies have reported that EEG synchrony is affected by Alzheimer's disease (AD). In this paper a frequency band analysis of AD EEG signals is presented, with the aim of improving the diagnosis of AD using EEG signals. In this paper, multiple synchrony measures are assessed through statistical tests (Mann-Whitney U test), including correlation, phase synchrony and Granger causality measures. Moreover, linear discriminant analysis (LDA) is conducted with those synchrony measures as features. For the data set at hand, the frequency range (5-6Hz) yields the best accuracy for diagnosing AD, which lies within the classical theta band (4-8Hz). The corresponding classification error is 4.88% for directed transfer function (DTF) Granger causality measure. Interestingly, results show that EEG of AD patients is more synchronous than in healthy subjects within the optimized range 5-6Hz, which is in sharp contrast with the loss of synchrony in AD EEG reported in many earlier studies. This new finding may provide new insights about the neurophysiology of AD. Additional testing on larger AD datasets is required to verify the effectiveness of the proposed approach.

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Section: Eeg Data Setmentioning

confidence: 99%

Diagnosis of Alzheimer's disease from EEG by means of synchrony measures in optimized frequency bands

Gallego-Jutglà

Elgendi

Vialatte

et al. 2012

2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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“…Three datasets containing EEG recordings of healthy subjects and AD patients at rest and with closed-eyes conditions were used [24,25,26]. These datasets differ in terms of the number of used electrodes and the sampling frequencies.…”

Section: Description Of Databasesmentioning

confidence: 99%

Stationary epoch-based entropy estimation for early diagnosis of Alzheimer's disease

Houmani

Vialatte

Latchoumane

et al. 2013

2013 IEEE Faible Tension Faible Consommation

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Several studies showed that EEG signal of Alzheimer's disease patients is less complex than that of healthy subjects. In this article, we propose to characterize the complexity of the EEG signal by an entropy measure based on local density estimation by a Hidden Markov Model. We first show that this measure leads to consistent results qualitatively and quantitatively (in terms of classification accuracy). Indeed, it discriminates AD patients, at an early stage of Alzheimer's disease, from healthy subjects: a classification accuracy of 80% is reached on a dataset including EEG data recorded in different conditions. Based on this measure, we also show that parietal and temporal regions are the first regions affected by complexity loss in the early stage of Alzheimer's disease.

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“…Finally, we a Hjorth-based index [12] using parameters activity, mobility and complexity [20] defined as: …”

Section: Methodsmentioning

confidence: 99%

Performance Evaluation and Fusion of Methods for Early Detection of Alzheimer Disease

Hamadicharef

Guan

Ifeachor

et al. 2008

2008 International Conference on BioMedical Engineering and Informatics

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P04.3 Characterisation of EEG at different stages of Alzheimer’s disease (AD)

Cited by 9 publications

References 0 publications

Diagnosis of Alzheimer's disease from EEG by means of synchrony measures in optimized frequency bands

Diagnosis of Alzheimer's disease from EEG by means of synchrony measures in optimized frequency bands

Stationary epoch-based entropy estimation for early diagnosis of Alzheimer's disease

Performance Evaluation and Fusion of Methods for Early Detection of Alzheimer Disease

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