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.