[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 …
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
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 …
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
Dementia is a chronic or progressive clinical syndrome, mainly characterized by the
deterioration of memory, thinking, reasoning and language. In Mild cognitive impairment …
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 …
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 …
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
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 …
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 …
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
Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by
cognitive decline, memory impairments, and behavioral changes. The presence of abnormal …
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.
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 …
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
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 …
has proven effective, the invasiveness and associated costs pose challenges for many …