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Microstate feature fusion for distinguishing AD from MCI
Yupan Shi1,2, Qinying Ma3,4, Chunyu Feng1,2
1Institute of Applied Mathematics, Hebei Academy of Sciences, Shijiazhuang, China.
Health Information Science and Systems
|August 1, 2022
Summary
Electroencephalogram (EEG) microstates show promise in measuring Alzheimer's disease (AD) severity and distinguishing it from mild cognitive impairment (MCI). These EEG features offer a potential neurobiological marker for AD diagnosis.
Area of Science:
- Neuroscience
- Computational Neuroscience
Background:
- Electroencephalogram (EEG) microstates offer rich temporal information for identifying neural features.
- Alzheimer's disease (AD) and mild cognitive impairment (MCI) require sensitive diagnostic markers.
Purpose of the Study:
- To investigate EEG microstates for measuring AD severity and differentiating AD from MCI.
- To evaluate the utility of microstate transition probabilities (TPs) and a novel time-factor transition probabilities (TTPs) feature.
Main Methods:
- Defined two features based on transition probabilities (TPs) to analyze microstate parameters.
- Assessed between-group differences in microstate temporal characteristics and within-group correlations with MMSE scores.
- Employed machine learning models with the TTPs feature and a partial accumulation strategy for classification.
Main Results:
- Significant between-group differences in microstate temporal characteristics were observed.
- Certain TPs correlated significantly with Mini-Mental State Examination (MMSE) scores within patient groups.
- The TTPs feature achieved high accuracy (0.938), sensitivity (0.923), and specificity (0.947) in distinguishing AD from MCI.
Conclusions:
- EEG microstates demonstrate potential as a neurobiological marker for Alzheimer's disease.
- Microstate analysis, particularly using TTPs, shows efficacy in assessing disease severity and aiding differential diagnosis between AD and MCI.
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