Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/98785
Title: Diagnosis of Alzheimer's disease from EEG by means of synchrony measures in optimized frequency bands
Authors: Cichocki, Andrzej
Gallego-Jutglà, Esteve
Elgendi, Mohamed
Vialatte, François-Benoît
Solé-Casals, Jordi
Latchoumane, Charles
Jeong, Jaeseung
Dauwels, Justin
Keywords: DRNTU::Engineering::Electrical and electronic engineering
Issue Date: 2012
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.
URI: https://hdl.handle.net/10356/98785
http://hdl.handle.net/10220/12524
DOI: 10.1109/EMBC.2012.6346909
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Conference Papers

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