TY - GEN
T1 - Multiresolution wavelet analysis and ensemble of classifiers for early diagnosis of Alzheimer's disease
AU - Jacques, Genevieve
AU - Frymiare, Jennifer L.
AU - Kounios, John
AU - Clark, Christopher
AU - Polikar, Robi
PY - 2005/1/1
Y1 - 2005/1/1
N2 - The diagnosis of Alzheimer's disease at an early stage is a major concern due to 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.
AB - The diagnosis of Alzheimer's disease at an early stage is a major concern due to 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.
UR - http://www.scopus.com/inward/record.url?scp=33646784010&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33646784010&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2005.1416322
DO - 10.1109/ICASSP.2005.1416322
M3 - Conference contribution
AN - SCOPUS:33646784010
SN - 0780388747
SN - 9780780388741
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - V389-V392
BT - 2005 IEEE ICASSP '05 - Proc. - Design and Implementation of Signal Proces.Syst.,Indust. Technol. Track,Machine Learning for Signal Proces. Education, Spec. Sessions
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP '05
Y2 - 18 March 2005 through 23 March 2005
ER -