失智症
痴呆
脑电图
心理学
生物标志物
认知
听力学
疾病
神经科学
医学
病理
生物化学
化学
作者
Andreas Miltiadous,Katerina D. Tzimourta,Νικόλαος Γιαννακέας,Markos G. Tsipouras,Theodora Afrantou,Panagiotis Ioannidis,Alexandros T. Tzallas
出处
期刊:Diagnostics
[MDPI AG]
日期:2021-08-09
卷期号:11 (8): 1437-1437
被引量:40
标识
DOI:10.3390/diagnostics11081437
摘要
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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