DEMNET NeuroDeep: Alzheimer detection using electroencephalogram and deep learning
Vaishali M. Joshi, Prajkta P. Dandavate, R. Rashmi, Gitanjali R. Shinde, Deepthi D. Kulkarni, Riddhi Mirajkar
Abstract
Alzheimer’s disease (AD) stands out as the most prevalent neurological brain disorder, and its diagnosis relies on various laboratory techniques. The electroencephalogram (EEG) emerges as a valuable tool for identifying AD, offering a quick, cost-effective, and readily accessible means of detecting early-stage dementia. Detecting AD in its early stages is crucial, as early intervention yields more successful outcomes and entails fewer risks than treating the disease at a later stage. The objective of this research is to create an advanced diagnosis system for AD using machine learning (ML) and EEG data. The proposed system utilizes a multilayer perceptron (MLP) and a deep neural network with bidirectional long short-term memory (BiLSTM) as the classifier. The feature extraction process involves incorporating Hjorth parameters, power spectral density (PSD), differential asymmetry (DASM), and differential entropy (DE). The BiLSTM classifier, particularly when combined with DE, exhibits outstanding performance with an accuracy of 97.27%. This amalgamation of DE and the deep neural network surpasses current state-of-the-art techniques, underscoring the substantial potential of this approach for precise and advanced diagnosis of AD.
Keywords
Alzheimer; Differential asymmetry; Differential entropy; Electroencephalogram; Hjorth parameters; Power spectral density
DOI:
https://doi.org/10.11591/eei.v14i1.8163
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Bulletin of EEI Stats
Bulletin of Electrical Engineering and Informatics (BEEI) ISSN: 2089-3191, e-ISSN: 2302-9285 This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU) .