Comparative multiresolution wavelet analysis of ERP spectral bands using an ensemble of classifiers approach for early diagnosis of Alzheimer's disease.
Early diagnosis of Alzheimer's disease (AD) is becoming an increasingly important healthcare concern. Prior approaches analyzing event-related potentials (ERPs) had varying degrees of success, primarily due to smaller study cohorts, and the inherent difficulty of the problem. A new effort using multiresolution analysis of ERPs is described. Distinctions of this study include analyzing a larger cohort, comparing different wavelets and different frequency bands, using ensemble-based decisions and, most importantly, aiming the earliest possible diagnosis of the disease. Surprising yet promising outcomes indicate that ERPs in response to novel sounds of oddball paradigm may be more reliable as a biomarker than the more commonly used responses to target sounds.
Polikar
Robi
R
Electrical and Computer Engineering, Rowan University, Glassboro, NJ 08028, USA. polikar@rowan.edu
Topalis
Apostolos
A
Green
Deborah
D
Kounios
John
J
Clark
Christopher M
CM
D000368:Aged; D000465:Algorithms; D000544:Alzheimer Disease / Q000145:classification* / Q000175:diagnosis; D001288:Attention / Q000502:physiology; D001931:Brain Mapping; D002540:Cerebral Cortex / Q000502:physiology*; D015331:Cohort Studies; D003196:Computer Graphics; D004569:Electroencephalography / Q000145:classification*; D018913:Event-Related Potentials, P300 / Q000502:physiology*; D005583:Fourier Analysis; D006801:Humans; D008297:Male; D008609:Mental Status Schedule; D008875:Middle Aged; D010898:Pitch Perception / Q000502:physiology; D012016:Reference Values; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted