Optimization of EEG frequency bands for improved diagnosis of Alzheimer disease

Elgendi, Mohamed; Vialatte, François; Cichocki, Andrzej; Latchoumane, Charles; Jeong, Jaesung; Dauwels, Justin

doi:10.1109/iembs.2011.6091504

Cited by 35 publications

(29 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Considering the fact that the relative PSD is positively correlated with the energy (Elgendi et al 2011;Vecchio et al 2003), the results obtained suggest that brains affected by AD show a much slower physiological behavior. These abnormalities could reflect two different pathophysiological changes: the relative PSD decrease for higher frequencies could be related to alterations in cortico-cortical connections, whereas the increase for lower frequencies could be related to the lack of influence of subcortical cholinergic structures on cortical electrical activity (Jeong 2004;Molnár et al 2006;Moretti et al 2009).…”

Section: Discussionmentioning

confidence: 84%

Power spectral density and coherence analysis of Alzheimer’s EEG

et al. 2014

View full text Add to dashboard Cite

In this paper, we investigate the abnormalities of electroencephalograph (EEG) signals in the Alzheimer's disease (AD) by analyzing 16-scalp electrodes EEG signals and make a comparison with the normal controls. The power spectral density (PSD) which represents the power distribution of EEG series in the frequency domain is used to evaluate the abnormalities of AD brain. Spectrum analysis based on autoregressive Burg method shows that the relative PSD of AD group is increased in the theta frequency band while significantly reduced in the alpha2 frequency bands, particularly in parietal, temporal, and occipital areas. Furthermore, the coherence of two EEG series among different electrodes is analyzed in the alpha2 frequency band. It is demonstrated that the pair-wise coherence between different brain areas in AD group are remarkably decreased. Interestingly, this decrease of pairwise electrodes is much more significant in inter-hemispheric areas than that in intra-hemispheric areas. Moreover, the linear cortico-cortical functional connectivity can be extracted based on coherence matrix, from which it is shown that the functional connections are obviously decreased, the same variation trend as relative PSD. In addition, we combine both features of the relative PSD and the normalized degree of functional network to discriminate AD patients from the normal controls by applying a support vector machine model in the alpha2 frequency band. It is indicated that the two groups can be clearly classified by the combined feature. Importantly, the accuracy of the classification is higher than that of any one feature. The obtained results show that analysis of PSD and coherence-based functional network can be taken as a potential comprehensive measure to distinguish AD patients from the normal, which may benefit our understanding of the disease.

show abstract

Section: Discussionmentioning

confidence: 84%

Power spectral density and coherence analysis of Alzheimer’s EEG

et al. 2014

View full text Add to dashboard Cite

show abstract

“…Some studies have analysed all the frequency bands between 1 and 30 Hz, for instance, using a power measure [13] or a set of synchrony measures [14]. The present study investigates whether the diagnosis of AD can be improved by analysing all possible frequency ranges in the 1-30Hz frequency range (e.g., 1-2 Hz, 1-3Hz, 1-4Hz….…”

Section: Compared To Other Systems Like Functional Magnetic Resonancementioning

confidence: 99%

A hybrid feature selection approach for the early diagnosis of Alzheimer’s disease

Gallego-Jutglà¹,

Solé-Casals²,

Vialatte³

et al. 2015

J. Neural Eng.

View full text Add to dashboard Cite

Objective. Recently, significant advances have been made in the early diagnosis of Alzheimer's disease from EEG. However, choosing suitable measures is a challenging task. Among other measures, frequency Relative Power and loss of complexity have been used with promising results. In the present study we investigate the early diagnosis of AD using synchrony measures and frequency Relative Power on EEG signals, examining the changes found in different frequency ranges. Approach. We first explore the use of a single feature for computing the classification rate, looking for the best frequency range. Then, we present a multiple feature classification system that outperforms all previous results using a feature selection strategy.These two approaches are tested in two different databases, one containing MCI and healthy subjects (patients age: 71.9 ± 10.2, healthy subjects age: 71.7 ± 8.3), and the other containing Mild AD and healthy subjects (patients age: 77.6 ± 10.0; healthy subjects age: 69.4± 11.5).Main Results. Using a single feature to compute classification rates we achieve a performance of 78.33% for the MCI data set and of 97.56 % for Mild AD. Results are clearly improved using the multiple feature classification, where a classification rate of 95% is found for the MCI data set using 11 features, and 100% for the Mild AD data set using 4 features. Significance. The new features selection method described in this work may be a reliable tool that could help to design a realistic system that does not require prior knowledge of a patient's status. With that aim, we explore the standardization of features for MCI and Mild AD data sets with promising results.

show abstract

“…In earlier work, we have used relative power of EEG in optimized frequency bands to detect mild AD [8]. We observed in that study that several narrow-band frequency ranges (4-7Hz, 8-15Hz and 19-24Hz) improve the detection accuracy.…”

mentioning

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

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Optimization of EEG frequency bands for improved diagnosis of Alzheimer disease

Cited by 35 publications

References 22 publications

Power spectral density and coherence analysis of Alzheimer’s EEG

Power spectral density and coherence analysis of Alzheimer’s EEG

A hybrid feature selection approach for the early diagnosis of Alzheimer’s disease

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

Contact Info

Product

Resources

About