Deep Learning and IoT-Enabled Framework for Accurate Classification and Monitoring of Alzheimer's Disease Based on EEG Signal Analysis

Alzheimer's disease is a brain ailment that impairs thinking, memory, and behaviour. The efficacy of Brain- Computer Interface (BCI) systems must be enhanced to increase their prevalence in the biomedical sector. The biomedical sector is crucial for precise and efficient patient diagnostics using digital signal processing and wireless sensor network technologies. Alzheimer's disease (AD) is an irreversible brain disorder. Inadequate Alzheimer's disease diagnosis in the early stages, together with exact identification, may aid in halting the progression of the illness. Precise and effective detection and categorization of Alzheimer's disease enhance quality of life and increase life expectancy for patients. The advanced deep learning technology surpasses traditional machine learning methods in classifying complex configurations in high-dimensional composite data, particularly in digital signal processing. The deep learning application for the early detection and accurate classification of Alzheimer's disease might be suggested for a neuron-signal methodology in extensive data analysis. This research paper proposes an intelligent decision-making system (IDMS) for the diagnosis and categorization of Alzheimer's disease (AD) and the continuous monitoring of patients utilizing connected wearable devices. Methods for measuring neuronal synchrony are proposed to diagnose and characterize Alzheimer's disease.