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Alzheimer's Diseases Risk Level Classifications Using Chaos Technique
Abstract
This paper aim to classify Alzheimer's diseases risk level determining Dementia and Epilepsy which is a collective term used to describe the problems that people with various underlying brain disorders or damage can have with their memory, language and thinking. Alzheimer disease is the best known and most common disorder under the umbrella of Dementia and Epilepsy. Brain functions are measured in the hope of determining and differentiating certain functional conditions of the brain. The raw EEG signals are sampled and parameter like energy, variance, peaks, sharp and spikes waves, duration, events and covariance are detected. In this classification it provides a better way in diagnosis of Epilepsy as well as Dementia patients. It also aims to safeguard the patient’s life during the critical situations. The aim of this work is to investigate possible metabolic impairment within the thalamus in patients suffering from both focal (mesial temporal lobe epilepsy with hippocampus sclerosis) and generalized epilepsy and dementia resulting from brain damage using Chaos-Voxel.
Keywords: EEG signals, epilepsy risk levels, fuzzy logic, chaotic optimization, Elman neural network
Cite this Article Jagrati Srivastava, Pradeep Tomar. Alzheimer's Diseases Risk Level Classifications Using Chaos Technique. Journal of Software Engineering Tools & Technology Trends. 2015; 2(2): 36–46p.
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