Proactive Alzheimer's Disease Identification Through EEG-Based Biomarkers

To detection of Alzheimer's disease (AD) therapy is crucial for patients. Because it enables patients to take prevention actions before brain damage takes place. Although inherited effects limit most computer detection methods, computers have been employed in numerous research to identify AD The “Electroencephalogram (EEG)” signal is used in this study to implement a deep learning detecting of Alzheimer's disease (AD). Initially, conventional internet databases are used to get the necessary EEG signal and there after used the The signal is divided into many current Neural Networks using the “Lifting Wavelet Transform (LWT)” decomposition level. Extracts the temporal properties from the signal that has been divided up.