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Updated: Nov 4, 2025
Automatic Detection of Highly Organized Theta Oscillations in the Murine EEG
Published on: March 10, 2017
Dynamic theta/beta ratio of clinical EEG in Alzheimer's disease
Yao Miao1, Peter Jurica1, Zbigniew R Struzik2
1RIKEN Brain Science Institute, 2-1 Hirosawa, Wako-shi, Saitama, 351-0198, Japan.
Background:
EEG of a resting state in Alzheimer disease (AD) patients and healthy controls (HC) are analyzed to identify the characteristics of EEG in AD.
New Method:
A dynamic box plot approach to the theta/beta ratio with various window durations is proposed to analyze EEG.
Results:
Spectral results during a resting state in AD patients demonstrate the effect of relatively greater power in the low-frequency bands (i.e. 'slowing down' of the EEG). A significant difference is observed in the dynamic distribution of the theta/beta ratio in the AD and HC groups, which is related to the effect of 'slowing down'. There is a more obvious visual separation between the theta/beta ratio results for the AD and HC groups with increasing window durations. Variability of the theta/beta ratio can be observed with shorter window durations with a dynamic functional box plot. This provides a better classification accuracy by using the dynamic theta/beta ratio as a sensor to discriminate AD EEG from HC EEG by using the receiver operating characteristics (ROC) curve and the area under curve (AUC) with various window durations.
Comparison With Existing Method(S):
EEG spectral analysis and theta/beta ratio used to evaluate EEG typically rely on long time averaging.
Conclusions:
The dynamic box plot approach to the theta/beta ratio with various window durations provides the possibility of observing features of the EEG. The dynamic theta/beta ratio is a better sensor to discriminate AD EEG from HC EEG. Moreover, the reliability and accuracy of results can be increased by combining spectral analysis and the dynamic box plot approach to theta/beta ratio with various window durations.
