Deep insights into MCI diagnosis: A comparative deep learning analysis of EEG time series.

Journal: Journal of neuroscience methods

Published Date: Jan 10, 2024

Abstract

BACKGROUND: Individuals in the early stages of Alzheimer's Disease (AD) are typically diagnosed with Mild Cognitive Impairment (MCI). MCI represents a transitional phase between normal cognitive function and AD. Electroencephalography (EEG) records carry valuable insights into cerebral cortex brain activities to analyze neuronal degeneration. To enhance the precision of dementia diagnosis, automatic and intelligent methods are required for the analysis and processing of EEG signals.

Authors

Mesut Şeker

Department of Electrical and Electronics Engineering, Dicle University, Diyarbakir, Turkey. Electronic address: mesut.seker@dicle.edu.tr.
Mehmet Siraç Özerdem

Department of Electrical and Electronics Engineering, Dicle University, Diyarbakir, Turkey. Electronic address: sozerdem@dicle.edu.tr.

Keywords

Alzheimer Disease Cognitive Dysfunction Deep Learning Electroencephalography Humans Neural Networks, Computer Time Factors

External Resources

View on PubMed Access via DOI PubMed (38215948)

Deep insights into MCI diagnosis: A comparative deep learning analysis of EEG time series.

Abstract

Authors

Keywords

External Resources

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