Paper Info
Title
Multiresolution wavelet analysis and ensemble of classifiers for early diagnosis of Alzheimer's disease
Abstract
The diagnosis of Alzheimer's disease at an early stage is a major concern due to the growing number of the elderly population affected, as well as the lack of a standard and effective diagnosis procedure available to community healthcare providers. Recent studies have used wavelets and other signal processing methods to analyze EEG signals in an attempt to find a non-invasive biomarker for Alzheimer's disease and had varying degrees of success. These studies have traditionally used automated classifiers such as neural networks; however the use of an ensemble of classifiers has not been previously explored and may prove to be beneficial. In this study, multiresolution wavelet analysis is performed on event related potentials of the EEG which are then used with the ensemble of classifiers based Learn++ algorithm. We describe the approach, and present our promising preliminary results.
Year
DOI
Venue
2005
10.1109/ICASSP.2005.1416322
ICASSP '05). IEEE International Conference
Keywords
Field
DocType
discrete wavelet transforms,electroencephalography,feature extraction,patient diagnosis,signal classification,Alzheimer's disease diagnosis,DWT,Daubechies wavelet,EEG event related potentials,EEG signal analysis,community healthcare,ensemble of classifiers based Learn++ algorithm,feature extraction,multiresolution wavelet analysis,noninvasive biomarker,quadratic b-spline wavelet
Signal processing,Population,Disease,Pattern recognition,Computer science,Feature extraction,Biomarker (medicine),Artificial intelligence,Artificial neural network,Electroencephalography,Machine learning,Wavelet
Conference
Volume
ISSN
ISBN
5
1520-6149
0-7803-8874-7
Citations 
PageRank 
References 
2
0.46
2
Authors
4
Name
Order
Citations
PageRank
Jacques, G.120.46
Frymiare, J.L.220.46
John Kounios3648.18
Christopher M Clark437629.76