An Automated Approach for the Detection of Alzheimer's Disease From Resting State Electroencephalography
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,...
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Published in | Frontiers in neuroinformatics Vol. 16; p. 924547 |
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Main Authors | , , , , , |
Format | Journal Article |
Language | English |
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11.07.2022
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Abstract | 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. |
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AbstractList | 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. 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 a self-driven classification pipeline. For this purpose, we recorded the resting state brain activity of twenty-six 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 self-driven 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. |
Author | Perez-Valero, Eduardo Lopez-Gordo, Miguel A López-Alcalde, Samuel Vílchez-Carrillo, Rosa M Morillas, Christian Carrera-Muñoz, Ismael |
AuthorAffiliation | 1 Department of Computers Architecture and Technology, University of Granada , Granada , Spain 5 Hospital Universitario San Rafael , Granada , Spain 4 Cognitive Neurology Group, Hospital Universitario Virgen de las Nieves , Granada , Spain 3 Department of Signal Theory, Telematics, and Communications, University of Granada , Granada , Spain 2 Brain Computer Interface Laboratory, Research Center for Information and Communications Technologies, University of Granada , Granada , Spain |
AuthorAffiliation_xml | – name: 4 Cognitive Neurology Group, Hospital Universitario Virgen de las Nieves , Granada , Spain – name: 2 Brain Computer Interface Laboratory, Research Center for Information and Communications Technologies, University of Granada , Granada , Spain – name: 3 Department of Signal Theory, Telematics, and Communications, University of Granada , Granada , Spain – name: 5 Hospital Universitario San Rafael , Granada , Spain – name: 1 Department of Computers Architecture and Technology, University of Granada , Granada , Spain |
Author_xml | – sequence: 1 givenname: Eduardo surname: Perez-Valero fullname: Perez-Valero, Eduardo – sequence: 2 givenname: Christian surname: Morillas fullname: Morillas, Christian – sequence: 3 givenname: Miguel A. surname: Lopez-Gordo fullname: Lopez-Gordo, Miguel A. – sequence: 4 givenname: Ismael surname: Carrera-Muñoz fullname: Carrera-Muñoz, Ismael – sequence: 5 givenname: Samuel surname: López-Alcalde fullname: López-Alcalde, Samuel – sequence: 6 givenname: Rosa M. surname: Vílchez-Carrillo fullname: Vílchez-Carrillo, Rosa M. |
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CitedBy_id | crossref_primary_10_1016_j_heliyon_2024_e26298 crossref_primary_10_3390_s23239361 crossref_primary_10_1038_s41598_023_27528_0 crossref_primary_10_3233_JAD_230525 crossref_primary_10_1142_S0129065723500211 crossref_primary_10_3233_JAD_230485 |
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SubjectTerms | Age Alzheimer's disease Automation Classification Cognition & reasoning Cognitive ability Dementia Discriminant analysis disease detection EEG Electroencephalography Entropy Hospitals machine learning Medical imaging Medical research Medical treatment Memory Neurodegenerative diseases Neuropsychology Neuroscience Signal processing Tomography |
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Title | An Automated Approach for the Detection of Alzheimer's Disease From Resting State Electroencephalography |
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