Combining EEG signal processing with supervised methods for Alzheimer's patients classification
Abstract Background Alzheimer's Disease (AD) is a neurodegenaritive disorder
characterized by a progressive dementia, for which actually no cure is known. An early …
characterized by a progressive dementia, for which actually no cure is known. An early …
EEG signal processing and supervised machine learning to early diagnose Alzheimer's disease
D Pirrone, E Weitschek, P Di Paolo, S De Salvo… - Applied sciences, 2022 - mdpi.com
Electroencephalography (EEG) signal analysis is a fast, inexpensive, and accessible
technique to detect the early stages of dementia, such as Mild Cognitive Impairment (MCI) …
technique to detect the early stages of dementia, such as Mild Cognitive Impairment (MCI) …
Diagnosis of Alzheimer's disease with Electroencephalography in a differential framework
N Houmani, F Vialatte, E Gallego-Jutglà, G Dreyfus… - PloS one, 2018 - journals.plos.org
This study addresses the problem of Alzheimer's disease (AD) diagnosis with
Electroencephalography (EEG). The use of EEG as a tool for AD diagnosis has been widely …
Electroencephalography (EEG). The use of EEG as a tool for AD diagnosis has been widely …
EEG window length evaluation for the detection of Alzheimer's disease over different brain regions
Alzheimer's Disease (AD) is a neurogenerative disorder and the most common type of
dementia with a rapidly increasing world prevalence. In this paper, the ability of several …
dementia with a rapidly increasing world prevalence. In this paper, the ability of several …
A dementia classification framework using frequency and time-frequency features based on EEG signals
Alzheimer's disease (AD) accounts for 60%–70% of all dementia cases, and clinical
diagnosis at its early stage is extremely difficult. As several new drugs aiming to modify …
diagnosis at its early stage is extremely difficult. As several new drugs aiming to modify …
Alzheimer's disease patients classification through EEG signals processing
Alzheimer's Disease (AD) and its preliminary stage-Mild Cognitive Impairment (MCI)-are the
most widespread neurodegenerative disorders, and their investigation remains an open …
most widespread neurodegenerative disorders, and their investigation remains an open …
Computational methods of EEG signals analysis for Alzheimer's disease classification
Computational analysis of electroencephalographic (EEG) signals have shown promising
results in detecting brain disorders, such as Alzheimer's disease (AD). AD is a progressive …
results in detecting brain disorders, such as Alzheimer's disease (AD). AD is a progressive …
Machine learning algorithms and statistical approaches for Alzheimer's disease analysis based on resting-state EEG recordings: A systematic review
Alzheimer's Disease (AD) is a neurodegenerative disorder and the most common type of
dementia with a great prevalence in western countries. The diagnosis of AD and its …
dementia with a great prevalence in western countries. The diagnosis of AD and its …
Feature selection before EEG classification supports the diagnosis of Alzheimer's disease
Objective In many decision support systems, some input features can be marginal or
irrelevant to the diagnosis, while others can be redundant among each other. Thus, feature …
irrelevant to the diagnosis, while others can be redundant among each other. Thus, feature …
Automated multiclass classification of spontaneous EEG activity in Alzheimer's disease and mild cognitive impairment
The discrimination of early Alzheimer's disease (AD) and its prodromal form (ie, mild
cognitive impairment, MCI) from cognitively healthy control (HC) subjects is crucial since the …
cognitive impairment, MCI) from cognitively healthy control (HC) subjects is crucial since the …