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Dauwels J.,Nanyang Technological University | Srinivasan K.,Nanyang Technological University | Srinivasan K.,Indian Institute of Technology Madras | Ramasubba Reddy M.,Indian Institute of Technology Madras | And 5 more authors.
International Journal of Alzheimer's Disease | Year: 2011

Medical studies have shown that EEG of Alzheimer's disease (AD) patients is "slower" (i.e., contains more low-frequency power) and is less complex compared to age-matched healthy subjects. The relation between those two phenomena has not yet been studied, and they are often silently assumed to be independent. In this paper, it is shown that both phenomena are strongly related. Strong correlation between slowing and loss of complexity is observed in two independent EEG datasets: (1) EEG of predementia patients (a.k.a. Mild Cognitive Impairment; MCI) and control subjects; (2) EEG of mild AD patients and control subjects. The two data sets are from different patients, different hospitals and obtained through different recording systems. The paper also investigates the potential of EEG slowing and loss of EEG complexity as indicators of AD onset. In particular, relative power and complexity measures are used as features to classify the MCI and MiAD patients versus age-matched control subjects. When combined with two synchrony measures (Granger causality and stochastic event synchrony), classification rates of 83 (MCI) and 98 (MiAD) are obtained. By including the compression ratios as features, slightly better classification rates are obtained than with relative power and synchrony measures alone. Copyright © 2011 Justin Dauwels et al. Source


Vialatte F.-B.,ESPCI ParisTech | Vialatte F.-B.,RIKEN | Dauwels J.,Nanyang Technological University | Maurice M.,RIKEN | And 2 more authors.
International Journal of Alzheimer's Disease | Year: 2011

Objective. EEG has great potential as a cost-effective screening tool for Alzheimer's disease (AD). However, the specificity of EEG is not yet sufficient to be used in clinical practice. In an earlier study, we presented preliminary results suggesting improved specificity of EEG to early stages of Alzheimer's disease. The key to this improvement is a new method for extracting sparse oscillatory events from EEG signals in the time-frequency domain. Here we provide a more detailed analysis, demonstrating improved EEG specificity for clinical screening of MCI (mild cognitive impairment) patients. Methods. EEG data was recorded of MCI patients and age-matched control subjects, in rest condition with eyes closed. EEG frequency bands of interest were θ(3.5-7.5Hz), α1 (7.5-9.5Hz), α2 (9.5-12.5Hz), and β(12.5-25Hz). The EEG signals were transformed in the time-frequency domain using complex Morlet wavelets; the resulting time-frequency maps are represented by sparse bump models. Results. Enhanced EEG power in the range is more easily detected through sparse bump modeling; this phenomenon explains the improved EEG specificity obtained in our previous studies. Conclusions. Sparse bump modeling yields informative features in EEG signal. These features increase the specificity of EEG for diagnosing AD. Copyright © 2011 Franois-B. Vialatte et al. Source


Ueda T.,Tokyo Institute of Technology | Musha T.,Brain Functions Laboratory Inc. | Yagi T.,Tokyo Institute of Technology
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS | Year: 2013

Mild cognitive impairment (MCI) patients and healthy people were classified by using a 'power variance function (PVF)', namely, an index of electroencephalography (EEG) proposed in a previous report. PVF is defined by calculating variance of the power variability of an EEG signal at each frequency of the signal using wavelet transform. After confirming that the distribution of PVFs of the subjects was a normal distribution at each frequency, the distributions of PVFs of 25 MCI patients and those of 57 healthy people were compared in terms of Z-score. The comparison results indicate that for the MCI patients, the PVFs in the θ band are significantly higher in left parieto-occipital area and that those in the β band are lower in the bitemporal area. Multidimensional discriminant analysis using the PVF in the θ-β band recorded only on four electrodes on the left parieto-occipital area could be used to classify MCI patients from healthy people with leave-one-out accuracy of 87.5%. This indicates the possibility of diagnosing MCI by using EEG signals recorded only on a few electrodes. © 2013 IEEE. Source


Musha T.,Brain Functions Laboratory Inc.
2015 International Conference on Noise and Fluctuations, ICNF 2015 | Year: 2015

Spontaneous electric potentials on the scalp (EEG: Electroencephalogram) are generated by cerebral neuronal activities. The EEG signals are therefore rich with information about cerebral activities. As long as the living body is alive, the biological rhythm is always subject to fluctuations. Furthermore, Neuronal activities carry biological information to maintain the living state [1]. We are now testing two novel technologies, which are based on EEG fluctuations for detecting the early stage of dementias, especially Alzheimer's disease (AD). Otherwise, there would be no other ways for its prevention. © 2015 IEEE. Source


Ueda T.,Tokyo Institute of Technology | Musha T.,Brain Functions Laboratory Inc. | Yagi T.,Tokyo Institute of Technology | Yagi T.,RIKEN
IEEJ Transactions on Electronics, Information and Systems | Year: 2010

In this paper, we proposed a new method for diagnosing Alzheimer's disease (AD) on the basis of electroencephalograms (EEG). The method, which is termed Power Variance Function (PVF) method, indicates the variance of the power at each frequency. By using the proposed method, the power of EEG at each frequency was calculated using Wavelet transform, and the corresponding variances were defined as PVF. After the PVF histogram of 55 healthy people was approximated as a Generalized Extreme Value (GEV) distribution, we evaluated the PVF of 22 patients with AD and 25 patients with mild cognitive impairment (MCI). As a result, the values for all AD and MCI subjects were abnormal. In particular, the PVF in the 6 band for MCI patients was abnormally high, and the PVF in the α band for AD patients was low. © 2010 The Institute of Electrical Engineers of Japan. Source

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