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EEG Signal for Diagnosing Diseases using Machine Learning

Authors(3):

Aswathy K J, Swathi Anil, Prof. Elizebath Issac
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Alzheimer's disease is a chronic neurodegenerative disease that usually starts slowly and worsens over time. Alzheimer’s is an irreversible, progressive brain disorder that slowly destroys memory and thinking skills, and eventually the ability to carry out the simplest tasks. In most people, symptom first appears in their mid-60s. Studies have reported that electroencephalogram (EEG) signals of Alzheimer’s disease patients usually have less synchronization when compare to healthy people. Changes in EEG signals start at early stage but, clinically, these changes are not easily detected. Early detection of Alzheimer’s can have treatments with more positive outcomes. The aim of this paper is to classify Alzheimer’s disease patients using EEG signal processing in order to support medical doctors in the right diagnosis formulation. The proposed system consists of mainly five steps: Signal Acquisition, Pre-processing, Feature Extraction, Feature Selection, and Classification. The Signal Acquisition makes use of EEG dataset, Band-pass-filtering is used in the pre-processing stage to get artifact free signal. The necessary features are then extracted from EEG signals using Wavelet Transform and they are subjected to Principal Component Analysis (PCA) for feature selection. The classification is done using Block Based Neural Network (BBNN). Based on the changes in EEG the structure and internal configuration of BBNN are modified.

Aswathy K J, Swathi Anil, Prof. Elizebath Issac

Alzheimer's disease (AD),EEG, Principal Component Analysis, BlockBased Neural Network.

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Publication Details

Published in : Volume 3 | Issue 7 | September - 2017
Date of Publication Print ISSN Online ISSN
2017-09-30 2395-1990 2394-4099
Page(s) Manuscript Number   Publisher
07-12 IJSRSET3702   Technoscience Academy

Cite This Article

Aswathy K J, Swathi Anil, Prof. Elizebath Issac, "EEG Signal for Diagnosing Diseases using Machine Learning", International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 3, Issue 7, pp.07-12, September-2017.
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