Investigation on performance analysis and comparison of KNN ANN and SVM classifiers for the Alzheimer disease classification from EEG signals

Abstract

Alzheimers Disease is a chronic neurological brain disorder and it is the most widely recognized reason for dementia Approximately 2 of the population experiencing the ill effects of the dementia under the age group of 65 and the chances of the dementia doubles after every five years Alzheimers Disease is a dynamic and irreversible brain disorder that gradually destroys the memory thinking skills and other cognitive skills which influences a persons capability to perform day by day activities These challenges take place on the nerve cells neurons and affect the parts of the brain involved in cognitive function which have been damaged or destroyed At the point when an individual has a side effect of dementia a doctor will perform direct tests to distinguish the reason The reason for the dementia is related to brain abnormalities The Electroencephalogram EEG signal is universally used as a diagnostic indicator for researching the brain activities under various physiological conditions This research work investigates to analyze the preprocessing of the EEG signal a feature extraction technique dimensionality reduction techniques and different classifiers for the classification of the EEG signals into the Alzheimer Disease patients and Healthy Control In the preprocessing of the EEG signal the concept of the multiplier is presented newline