An Automated Approach for the Detection of Alzheimer's Disease From Resting State Electroencephalography.

Autor:	Perez-Valero E; Department of Computers Architecture and Technology, University of Granada, Granada, Spain.; Brain Computer Interface Laboratory, Research Center for Information and Communications Technologies, University of Granada, Granada, Spain., Morillas C; Department of Computers Architecture and Technology, University of Granada, Granada, Spain.; Brain Computer Interface Laboratory, Research Center for Information and Communications Technologies, University of Granada, Granada, Spain., Lopez-Gordo MA; Brain Computer Interface Laboratory, Research Center for Information and Communications Technologies, University of Granada, Granada, Spain.; Department of Signal Theory, Telematics, and Communications, University of Granada, Granada, Spain., Carrera-Muñoz I; Cognitive Neurology Group, Hospital Universitario Virgen de las Nieves, Granada, Spain., López-Alcalde S; Hospital Universitario San Rafael, Granada, Spain., Vílchez-Carrillo RM; Cognitive Neurology Group, Hospital Universitario Virgen de las Nieves, Granada, Spain.
Jazyk:	angličtina
Zdroj:	Frontiers in neuroinformatics [Front Neuroinform] 2022 Jul 11; Vol. 16, pp. 924547. Date of Electronic Publication: 2022 Jul 11 (Print Publication: 2022).
DOI:	10.3389/fninf.2022.924547
Abstrakt:	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. Furthermore, they require laborious analysis, what delays the start of medical treatment. To overcome this, researchers have recently investigated AD detection based on electroencephalography, a non-invasive neurophysiology technique, and machine learning algorithms. However, these approaches typically rely on manual procedures such as visual inspection, that requires additional personnel for the analysis, or on cumbersome EEG acquisition systems. In this paper, we performed a preliminary evaluation of a fully-automated approach for AD detection based on a commercial EEG acquisition system and an automated classification pipeline. For this purpose, we recorded the resting state brain activity of 26 participants from three groups: mild AD, mild cognitive impairment (MCI-non-AD), and healthy controls. First, we applied automated data-driven algorithms to reject EEG artifacts. Then, we obtained spectral, complexity, and entropy features from the preprocessed EEG segments. Finally, we assessed two binary classification problems: mild AD vs. controls, and MCI-non-AD vs. controls, through leave-one-subject-out cross-validation. The preliminary results that we obtained are comparable to the best reported in literature, what suggests that AD detection could be automatically detected through automated processing and commercial EEG systems. This is promising, since it may potentially contribute to reducing costs related to AD screening, and to shortening detection times, what may help to advance medical treatment. Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (Copyright © 2022 Perez-Valero, Morillas, Lopez-Gordo, Carrera-Muñoz, López-Alcalde and Vílchez-Carrillo.)
Databáze:	MEDLINE
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