Alzheimer’s Disease and Frontotemporal Dementia: A Robust Classification Method of EEG Signals and a Comparison of Validation Methods

Dementia is the clinical syndrome characterized by progressive loss of cognitive and emotional abilities to a degree severe enough to interfere with daily functioning. Alzheimer’s disease (AD) is the most common neurogenerative disorder, making up 50–70% of total dementia cases. Another dementia typ...

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Main Authors: Andreas Miltiadous, Katerina D. Tzimourta, Nikolaos Giannakeas, Markos G. Tsipouras, Theodora Afrantou, Panagiotis Ioannidis, Alexandros T. Tzallas
Format: Article
Language:English
Published: MDPI AG 2021-08-01
Series:Diagnostics
Subjects:
Online Access:https://www.mdpi.com/2075-4418/11/8/1437
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author Andreas Miltiadous
Katerina D. Tzimourta
Nikolaos Giannakeas
Markos G. Tsipouras
Theodora Afrantou
Panagiotis Ioannidis
Alexandros T. Tzallas
author_facet Andreas Miltiadous
Katerina D. Tzimourta
Nikolaos Giannakeas
Markos G. Tsipouras
Theodora Afrantou
Panagiotis Ioannidis
Alexandros T. Tzallas
author_sort Andreas Miltiadous
collection DOAJ
description Dementia is the clinical syndrome characterized by progressive loss of cognitive and emotional abilities to a degree severe enough to interfere with daily functioning. Alzheimer’s disease (AD) is the most common neurogenerative disorder, making up 50–70% of total dementia cases. Another dementia type is frontotemporal dementia (FTD), which is associated with circumscribed degeneration of the prefrontal and anterior temporal cortex and mainly affects personality and social skills. With the rapid advancement in electroencephalogram (EEG) sensors, the EEG has become a suitable, accurate, and highly sensitive biomarker for the identification of neuronal and cognitive dynamics in most cases of dementia, such as AD and FTD, through EEG signal analysis and processing techniques. In this study, six supervised machine-learning techniques were compared on categorizing processed EEG signals of AD and FTD cases, to provide an insight for future methods on early dementia diagnosis. K-fold cross validation and leave-one-patient-out cross validation were also compared as validation methods to evaluate their performance for this classification problem. The proposed methodology accuracy scores were 78.5% for AD detection with decision trees and 86.3% for FTD detection with random forests.
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spelling doaj.art-b475f04708df4f11a0fe66859587f5a12023-11-22T07:20:29ZengMDPI AGDiagnostics2075-44182021-08-01118143710.3390/diagnostics11081437Alzheimer’s Disease and Frontotemporal Dementia: A Robust Classification Method of EEG Signals and a Comparison of Validation MethodsAndreas Miltiadous0Katerina D. Tzimourta1Nikolaos Giannakeas2Markos G. Tsipouras3Theodora Afrantou4Panagiotis Ioannidis5Alexandros T. Tzallas6Department of Informatics and Telecommunications, School of Informatics and Telecommunications, University of Ioannina, Kostakioi, 47 100 Arta, GreeceDepartment of Electrical and Computer Engineering, Faculty of Engineering, University of Western Macedonia, 50 100 Kozani, GreeceDepartment of Informatics and Telecommunications, School of Informatics and Telecommunications, University of Ioannina, Kostakioi, 47 100 Arta, GreeceDepartment of Electrical and Computer Engineering, Faculty of Engineering, University of Western Macedonia, 50 100 Kozani, Greece2nd Department of Neurology, AHEPA University Hospital, Aristotle University of Thessaloniki, GR54636 Thessaloniki, Greece2nd Department of Neurology, AHEPA University Hospital, Aristotle University of Thessaloniki, GR54636 Thessaloniki, GreeceDepartment of Informatics and Telecommunications, School of Informatics and Telecommunications, University of Ioannina, Kostakioi, 47 100 Arta, GreeceDementia is the clinical syndrome characterized by progressive loss of cognitive and emotional abilities to a degree severe enough to interfere with daily functioning. Alzheimer’s disease (AD) is the most common neurogenerative disorder, making up 50–70% of total dementia cases. Another dementia type is frontotemporal dementia (FTD), which is associated with circumscribed degeneration of the prefrontal and anterior temporal cortex and mainly affects personality and social skills. With the rapid advancement in electroencephalogram (EEG) sensors, the EEG has become a suitable, accurate, and highly sensitive biomarker for the identification of neuronal and cognitive dynamics in most cases of dementia, such as AD and FTD, through EEG signal analysis and processing techniques. In this study, six supervised machine-learning techniques were compared on categorizing processed EEG signals of AD and FTD cases, to provide an insight for future methods on early dementia diagnosis. K-fold cross validation and leave-one-patient-out cross validation were also compared as validation methods to evaluate their performance for this classification problem. The proposed methodology accuracy scores were 78.5% for AD detection with decision trees and 86.3% for FTD detection with random forests.https://www.mdpi.com/2075-4418/11/8/1437electroencephalogramEEGdementiaAlzheimer’s diseasefrontotemporal dementiaclassification
spellingShingle Andreas Miltiadous
Katerina D. Tzimourta
Nikolaos Giannakeas
Markos G. Tsipouras
Theodora Afrantou
Panagiotis Ioannidis
Alexandros T. Tzallas
Alzheimer’s Disease and Frontotemporal Dementia: A Robust Classification Method of EEG Signals and a Comparison of Validation Methods
Diagnostics
electroencephalogram
EEG
dementia
Alzheimer’s disease
frontotemporal dementia
classification
title Alzheimer’s Disease and Frontotemporal Dementia: A Robust Classification Method of EEG Signals and a Comparison of Validation Methods
title_full Alzheimer’s Disease and Frontotemporal Dementia: A Robust Classification Method of EEG Signals and a Comparison of Validation Methods
title_fullStr Alzheimer’s Disease and Frontotemporal Dementia: A Robust Classification Method of EEG Signals and a Comparison of Validation Methods
title_full_unstemmed Alzheimer’s Disease and Frontotemporal Dementia: A Robust Classification Method of EEG Signals and a Comparison of Validation Methods
title_short Alzheimer’s Disease and Frontotemporal Dementia: A Robust Classification Method of EEG Signals and a Comparison of Validation Methods
title_sort alzheimer s disease and frontotemporal dementia a robust classification method of eeg signals and a comparison of validation methods
topic electroencephalogram
EEG
dementia
Alzheimer’s disease
frontotemporal dementia
classification
url https://www.mdpi.com/2075-4418/11/8/1437
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