title: Quantifying the similarity of multiple point processes with application to early diagnosis of Alzheimer's disease from EEG.
abstract: A novel approach is proposed to quantify the similarity (or 'synchrony') of multiple multi-dimensional point processes. It is based on a generative stochastic model that describes how two or more point processes are related to each other. As an application, the problem of diagnosing Alzheimer's disease (AD) from multi-channel EEG recordings is considered. The proposed method seems to be more sensitive to AD induced perturbations in EEG synchrony than classical similarity measures.
authors.affiliation: MIT, Cambridge, MA 02139, USA. jdauwels@mit.edu
authors.forename: Justin
authors.initials: J
authors.lastname: Dauwels
authors.forename: Theophane
authors.initials: T
authors.lastname: Weber
authors.forename: Francois
authors.initials: F
authors.lastname: Vialatte
authors.forename: Andrzej
authors.initials: A
authors.lastname: Cichocki
mesh_terms: D000544:Alzheimer Disease / Q000175:diagnosis* / Q000503:physiopathology; D002540:Cerebral Cortex / Q000503:physiopathology*; D003627:Data Interpretation, Statistical; D042241:Early Diagnosis; D004569:Electroencephalography / Q000379:methods*; D001330:Electronic Data Processing; D006801:Humans; D016014:Linear Models; D015233:Models, Statistical; D008962:Models, Theoretical; D011237:Predictive Value of Tests; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted / Q000295:instrumentation*; D013057:Spectrum Analysis; D013269:Stochastic Processes