A study on the application of cross-frequency coupling characteristics of neural oscillation in the diagnosis of mild cognitive impairment.
10.7507/1001-5515.202210020
- Author:
Xin LI
1
;
Kai WANG
1
;
Jun JING
1
;
Liyong YIN
2
;
Ying ZHANG
3
;
Ping XIE
1
Author Information
1. School of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei 066004, P. R. China.
2. The First Hospital of Qinhuangdao, Qinhuangdao, Hebei 066004, P. R. China.
3. Engineering Training Centre, Yanshan University, Qinhuangdao, Hebei 066004, P. R. China.
- Publication Type:Journal Article
- Keywords:
Batch normalization;
Cross-frequency coupling;
Data enhancement;
Phase locking value
- MeSH:
Humans;
Electroencephalography/methods*;
Cognitive Dysfunction/diagnosis*;
Neural Networks, Computer;
Sensitivity and Specificity
- From:
Journal of Biomedical Engineering
2023;40(5):843-851
- CountryChina
- Language:Chinese
-
Abstract:
In order to fully explore the neural oscillatory coupling characteristics of patients with mild cognitive impairment (MCI), this paper analyzed and compared the strength of the coupling characteristics for 28 MCI patients and 21 normal subjects under six different-frequency combinations. The results showed that the difference in the global phase synchronization index of cross-frequency coupling under δ-θ rhythm combination was statistically significant in the MCI group compared with the normal control group ( P = 0.025, d = 0.398). To further validate this coupling feature, this paper proposed an optimized convolutional neural network model that incorporated a time-frequency data enhancement module and batch normalization layers to prevent overfitting while enhancing the robustness of the model. Based on this optimized model, with the phase locking value matrix of δ-θ rhythm combination as the single input feature, the diagnostic accuracy of MCI patients was (95.49 ± 4.15)%, sensitivity and specificity were (93.71 ± 7.21)% and (97.50 ± 5.34)%, respectively. The results showed that the characteristics of the phase locking value matrix under the combination of δ-θ rhythms can adequately reflect the cognitive status of MCI patients, which is helpful to assist the diagnosis of MCI.
