Multiresolution analysis for early diagnosis of Alzheimer's disease.
Early diagnosis of Alzheimer's disease is a major concern due to large portions of the elderly population it affects and the lack of a standard and effective diagnosis procedure that is available to community healthcare providers. Several studies have been performed using wavelets or other signal processing methods to analyze EEG signals in an attempt to find a biomarker for Alzheimer's disease, which showed varying degrees of success. To date, in part due to lack of a large study cohort, the results of these studies remain largely inconclusive. In this paper, we describe a new effort using multiresolution wavelet analysis on event related potentials of the EEG to investigate whether such a link can be established. Several factors sets this study apart from similar prior efforts: We use a larger cohort, compare different mother wavelets, rather then using one generic wavelet, and most importantly, we specifically target early diagnosis of the disease. Our multi-year effort will include a total of 80 patients, whose ERPs will be analyzed with several different wavelets and automated classifiers. We present some preliminary, yet very promising, results on analysis of EEGs of the first 28 patients analyzed thus far using two types of wavelets.
Jacques
Genevieve
G
Dept. of Electr. & Comput. Eng., Rowan Univ., Glassboro, NJ, USA.
Frymiare
Jennifer L
JL
Kounios
John
J
Clark
Christopher
C
Polikar
Robi
R