This hybrid method is a good tool for describing and distinguishing the AD EEG activity along its different stages.
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In this study, Electroencephalogram (EEG) signals have been used to serve as a way of finding parameters to improve AD diagnosis in first stages. For that, a ...
May 20, 2023 · Computational analysis of electroencephalographic (EEG) signals have shown promising results in detecting brain disorders, such as Alzheimer's disease (AD).
We investigate the early diagnosis of AD using synchrony measures and frequency RP on EEG signals, examining the changes found in different frequency ranges.
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This paper reviews the role of electroencephalogram as a biomarker based on signal processing to detect dementia in early stages and classify its severity.
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Mar 4, 2024 · We developed a computer-based cognitive task to classify the presence or absence of AD pathology and stage within the AD spectrum.
Feb 10, 2023 · Our study indicates that EEG can be used as a useful screening tool for the diagnosis and disease progression evaluation of MCI and AD.
Early Alzheimer's disease diagnosis based on EEG spectral images ... A hybrid feature selection approach for the early diagnosis of Alzheimer's disease.
In this study, Electroencephalogram (EEG) signals have been used to serve as a way of finding parameters to improve AD diagnosis in first stages. For that, a ...
Sep 25, 2023 · Our proposed method attains an impressive 80.23% classification accuracy in distinguishing between AD, Frontotemporal dementia (FTD), and Normal Control (NC) ...