ABSTRACT
MULTIRESOLUTION WAVELET ANALYSIS
OF
EVENT RELATED POTENTIALS
FOR THE DETECTION OF
ALZHEIMER'S DISEASE
by
Abstract
Alzheimer's disease is a neurological disorder (dementia),
usually seen in elderly people, which can only be diagnosed
(for sure) via an autopsy, i.e., after the patient has died.
Neurologists can usually diagnose Alzheimer's disease from
symptoms, however, misdiagnosis is quite common. Although
Alzheimer's cannot be cured, early detection of the
disease can help in delaying the progress of the disease.
In this study, we have tried to diagnose the disease by
analyzing the electroencephalograms (EEG) of the patients.
The signals acquired with the "oddball paradigm" experiment
were analyzed using multiresolution wavelet analysis techniques.
Multiresolution wavelet analysis is a relatively new technique
in signal processing, especially suited for the analysis of
non-stationary signals. It offers a represention of signals in
the time-frequency plane, giving information regarding the time
localization of the spectral components. EEG signals are highly
non-stationary in nature, and therefore, they are not suited for
Fourier transform based analysis.
The analyzed signals were then used to train a neural network.
Both supervised and unsupervised learning rules were used to
distinguish the signals that belong to patients with Alzheimer's
disease from the ones that belong to control patients without
Alzheimer's disease. Initial results are very promising, and
demonstrate the feasibility of this approach.
There has been very little study on early detection of Alzheimer's
disease by using signal processing techniques, and we believe that
future study on this approach will result in more reliable diagnosis of
Alzheimer's disease.
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