[PDF][PDF] Enyhe kognitív zavar automatikus felismerése szekvenciális autoenkóder használatával

M Kiss-Vetráb, EL José Vicente, R Balogh, N Imre… - 2022 - real.mtak.hu
Kivonat Az enyhe kognitív zavar (EKZ) hetegorén klinikai szindróma. Főbb tünetei közé
tartozik a memória, a gondolkodás, az érvelés és a nyelvi képességek romlása, amely …

Automated alzheimer's disease diagnosis using norm features extracted from EEG signals

R Ranjan, BC Sahana - 2023 14th international conference on …, 2023 - ieeexplore.ieee.org
Alzheimer's disease (AD) is a neurodegenerative disorder that progresses over time and
affects cognitive abilities. It is marked by symptoms such as memory loss, language and …

Using spectral sequence-to-sequence autoencoders to assess mild cognitive impairment

M Vetráb, JV Egas-López, R Balogh… - ICASSP 2022-2022 …, 2022 - ieeexplore.ieee.org
Dementia is a chronic or progressive clinical syndrome, mainly characterized by the
deterioration of memory, thinking, reasoning and language. In Mild cognitive impairment …

An automated approach for the detection of Alzheimer's disease from resting state electroencephalography

E Perez-Valero, C Morillas, MA Lopez-Gordo… - Frontiers in …, 2022 - frontiersin.org
Early detection is crucial to control the progression of Alzheimer's disease and to postpone
intellectual decline. Most current detection techniques are costly, inaccessible, or invasive …

[HTML][HTML] Adazd-Net: Automated adaptive and explainable Alzheimer's disease detection system using EEG signals

SK Khare, UR Acharya - Knowledge-Based Systems, 2023 - Elsevier
Background: Alzheimer's disease (AZD) is a degenerative neurological condition that
causes dementia and leads the brain to atrophy. Although AZD cannot be cured, early …

An improved I-FAST system for the diagnosis of Alzheimer's disease from unprocessed electroencephalograms by using robust invariant features

M Buscema, F Vernieri, G Massini, F Scrascia… - Artificial intelligence in …, 2015 - Elsevier
Objective This paper proposes a new, complex algorithm for the blind classification of the
original electroencephalogram (EEG) tracing of each subject, without any preliminary pre …

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 …

Comparative analysis of machine learning algorithms for Alzheimer's disease classification using EEG signals and genetic information

WY Yu, TH Sun, KC Hsu, CC Wang, SY Chien… - Computers in Biology …, 2024 - Elsevier
Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by
cognitive decline, memory impairments, and behavioral changes. The presence of abnormal …

[PDF][PDF] High-Performance Detection of Mild Cognitive Impairment Using Resting-State EEG Signals Located in Broca's Area: A Machine Learning Approach.

J Gross, N Groiss, T Rieg, H Baumgartl, R Buettner - AMCIS, 2021 - scholar.archive.org
Dementia and Alzheimer's disease represent one of the biggest medical challenges of our
century, manifesting the risk to individuals of losing their language or selfmanagement skills …

Early Detection of Alzheimer's Disease through Analysis of EEG Responses to Word Recognition

H Jang, SK Kim, J Ha, L Kim - 2024 12th International Winter …, 2024 - ieeexplore.ieee.org
Early detection is crucial in addressing Alzheimer's disease (AD). Although regular testing
has proven effective, the invasiveness and associated costs pose challenges for many …