title: A theta-band EEG based index for early diagnosis of Alzheimer's disease.
abstract: 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 α 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.
authors.affiliation: Data and Signal Processing Group, University of Vic - Central University of Catalonia, Vic, Catalonia, Spain.
authors.forename: Esteve
authors.initials: E
authors.lastname: Gallego-Jutglà
authors.affiliation: Data and Signal Processing Group, University of Vic - Central University of Catalonia, Vic, Catalonia, Spain.
authors.forename: Jordi
authors.initials: J
authors.lastname: Solé-Casals
authors.affiliation: ESPCI ParisTech, Laboratoire SIGMA, Paris, France.
authors.forename: François-Benoît
authors.initials: FB
authors.lastname: Vialatte
authors.affiliation: Nanyang Technological University, Singapore, Singapore.
authors.forename: Justin
authors.initials: J
authors.lastname: Dauwels
authors.affiliation: BSI RIKEN ABSP Lab, Wako, Saitama, Japan Systems Research Institute of Polish Academy of Science, Warsaw, Poland.
authors.forename: Andrzej
authors.initials: A
authors.lastname: Cichocki
mesh_terms: D000368:Aged; D000544:Alzheimer Disease / Q000175:diagnosis* / Q000503:physiopathology*; D001921:Brain / Q000503:physiopathology*; D060825:Cognitive Dysfunction / Q000175:diagnosis / Q000503:physiopathology; D003936:Diagnosis, Computer-Assisted / Q000379:methods; D042241:Early Diagnosis; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D008609:Mental Status Schedule; D013826:Theta Rhythm / Q000502:physiology*
keywords: Alzheimer's disease; data interpretation; electroencephalography; mild cognitive impairment; phase synchronization