A Greedy Optimized Intelligent Framework for Early Detection of Alzheimer’s Disease Using EEG Signal

Abstract

Recent researchers have been drawn to the analysis of electroencephalogram (EEG) signals in order to confirm the disease and severity range by viewing the EEG signal which has complicated the dataset. The conventional models such as machine learning, classifiers, and other mathematical models achieved the lowest classification score. The current study proposes to implement a novel deep feature with the best solution for EEG signal analysis and severity specification. A greedy sandpiper-based recurrent neural system (SbRNS) model for predicting Alzheimer’s disease (AD) severity has been proposed. The filtered data are used as input for the feature analysis and the severity range is divided into three classes: low, medium, and high. The designed approach was then implemented in the matrix laboratory (MATLAB) system, and the effectiveness score was calculated using key metrics such as precision, recall, specificity, accuracy, and misclassification score. The validation results show that the proposed scheme achieved the best classification outcome.

Developing a Modified Deep Belief Network with metaheuristic optimization Algorithm for predicting Alzheimer disease using Electroencephalogram

“…For the early diagnosis of Alzheimer's disease from an EEG signal, R. Swarnalatha proposed A Greedy Optimized Intelligent Framework [29]. To classify the varying degrees of AD severity using EEG signals, a specialized SbRNS was built in the MATLAB environment, and the severity investigation method was applied.…”