Despite recent advances, early diagnosis of Alzheimer’s disease (AD) from electroencephalography (EEG) remains
a difficult task. In this paper, we offer an added measure through which such early diagnoses can potentially be improved. One
feature that has been used for discriminative classification is changes in EEG synchrony. ...»»»»
Despite recent advances, early diagnosis of Alzheimer’s disease (AD) from electroencephalography (EEG) remains
a difficult task. In this paper, we offer an added measure through which such early diagnoses can potentially be improved. One
feature that has been used for discriminative classification is changes in EEG synchrony. So far, only the decrease of synchrony
in the higher frequencies has been deeply analyzed. In this paper, we investigate the increase of synchrony found in narrow
frequency ranges within the θ band. This particular increase of synchrony is used with the well-known decrease of synchrony
in the band to enhance detectable differences between AD patients and healthy subjects. We propose a new synchrony ratio
that maximizes the differences between two populations. The ratio is tested using two different data sets, one of them containing
mild cognitive impairment patients and healthy subjects, and another one, containing mild AD patients and healthy subjects.
The results presented in this paper show that classification rate is improved, and the statistical difference between AD patients
and healthy subjects is increased using the proposed ratio.^^^^
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Citació Bibliogràfica:
Gallego-Jutglà, E., Solé-Casals, J., Vialatte, F. -., Dauwels, J., & Cichocki, A. (2015). A theta-band EEG based index for early diagnosis of alzheimer's disease. Journal of Alzheimer's Disease, 43(4), 1175-1184.