Deep Neural Network Model for Automated Detection of Alzheimer’s Disease using EEG Signals

Abstract

Our brain is our body’s control centre and is essential for proper functioning of the body. Alzheimer’s disease is a chronic neurodegenerative disease that affects the cerebral cortex of the brain and causes memory loss and loss of cognitive thinking. EEG (Electroencephalography) is a method of recording neurological electrical activity with electrodes. It was chosen as it is a simple, painless procedure. This paper suggests an automated and accurate algorithm for the detection of Alzheimer's Disease using EEG signals with a combination of Signal processing and Deep Learning Methods. Concepts like Butterworth filters, DWT, statistical parameters, Data Augmentation and CNN were used in order to achieve a classification algorithm with high accuracy. A total highest  system  accuracy of 97.61% was achieved.