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

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dc.authorid0000-0001-9245-6790en_US
dc.authorid0000-0002-9368-8902en_US
dc.contributor.authorŞeker, Mesut
dc.contributor.authorÖzerdem, Mehmet Siraç
dc.date.accessioned2024-02-22T06:31:25Z
dc.date.available2024-02-22T06:31:25Z
dc.date.issuedMart 2024en_US
dc.departmentDicle Üniversitesi, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.description.abstractBackground 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. New methods This paper aims to address the challenges associated with MCI diagnosis by leveraging EEG signals and deep learning techniques. The analysis in this study focuses on processing the information embedded within the sequence of raw EEG time series data. EEG recordings are collected from 10 Healthy Controls (HC) and 10 MCI participants using 19 electrodes during a 30 min eyes-closed session. EEG time series are transformed into 2 separate formats of input tensors and applied to deep neural network architectures. Convolutional Neural Network (CNN) and ResNet from scratch are performed with 2D time series with different segment lengths. Furthermore, EEGNet and DeepConvNet architectures are utilized for 1D time series. Results ResNet demonstrates superior effectiveness in detecting MCI when compared to CNN architecture. Complete discrimination is achieved using EEGNet and DeepConvNet for noisy segments. Comparison with existing methods ResNet has yielded a 3 % higher accuracy rate compared to CNN. None of the architectures in the literature have achieved 100 % accuracy except proposed EEGNet and DeepConvnet. Conclusion Deep learning architectures hold great promise in enhancing the accuracy of early MCI detection.en_US
dc.identifier.citationŞeker, M. ve Özerdem, M.S. (2024). Deep insights into MCI diagnosis: A comparative deep learning analysis of EEG time series. Journal of Neuroscience Methods, 403, 110057.en_US
dc.identifier.doi10.1016/j.jneumeth.2024.110057
dc.identifier.endpage11en_US
dc.identifier.issn0165-0270
dc.identifier.pmid38215948
dc.identifier.scopus2-s2.0-85184279446
dc.identifier.scopusqualityQ2
dc.identifier.startpage1en_US
dc.identifier.urihttps://www.sciencedirect.com/science/article/pii/S0165027024000025
dc.identifier.urihttps://hdl.handle.net/11468/13354
dc.identifier.volume403en_US
dc.identifier.wosWOS:001164111200001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.institutionauthorŞeker, Mesut
dc.institutionauthorÖzerdem, Mehmet Siraç
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofJournal of Neuroscience Methods
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/embargoedAccessen_US
dc.subjectEEGen_US
dc.subjectMild Cognitive Impairmenten_US
dc.subjectCNNen_US
dc.subjectResNeten_US
dc.subjectEEGNeten_US
dc.subjectDeepConvNeten_US
dc.titleDeep insights into MCI diagnosis: A comparative deep learning analysis of EEG time seriesen_US
dc.titleDeep insights into MCI diagnosis: A comparative deep learning analysis of EEG time series
dc.typeArticleen_US

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