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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Bibliographic Details
Published in:Diagnostics (Basel) 2021-08, Vol.11 (8), p.1437
Main Authors: Miltiadous, Andreas, Tzimourta, Katerina D., Giannakeas, Nikolaos, Tsipouras, Markos G., Afrantou, Theodora, Ioannidis, Panagiotis, Tzallas, Alexandros T.
Format: Article
Language:English
Subjects:
Accuracy
Age
Algorithms
Alzheimer's disease
Aphasia
Bibliographic literature
Brain research
Classification
Decision trees
Dementia
Discriminant analysis
EEG
Electrodes
electroencephalogram
Electroencephalography
frontotemporal dementia
Medical imaging
Methods
Neural networks
Signal processing
Support vector machines
Wavelet transforms
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Summary: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.
ISSN:2075-4418
2075-4418
DOI:10.3390/diagnostics11081437