Extracting the Features of Diseases Using Fast Fourier Transform and Convolutional Neural Network

Abstract

Alzheimer's disease causes long-term harm to brain cells, particularly in the hippocampus. In persons aged 65 and up, this disease is the leading cause of dementia. This neurological disorder worsens with time and cannot be cured. Alzheimer's disease can cause irreversible brain damage, therefore catching it early is crucial. In order to stop the progression of the disease before it becomes irreversible, prompt and precise therapy is required. In this paper, we offer a Deep Learning-based method for enhancing the precision of classification by the use of the Convolutional Neural Network (CNN). In this study, we examine the EEG data, extract characteristics using Fast Fourier Transform (FFT), and then use convolutional neural networks (CNNs) to categorize the condition.