Quantifying the similarity of multiple point processes with application to early diagnosis of Alzheimer's disease from EEG.
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.
Dauwels
Justin
J
MIT, Cambridge, MA 02139, USA. jdauwels@mit.edu
Weber
Theophane
T
Vialatte
Francois
F
Cichocki
Andrzej
A
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