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. 2022 Jan 29:2022:9398551.
doi: 10.1155/2022/9398551. eCollection 2022.

Intelligent Algorithm-Based Quantitative Electroencephalography in Evaluating Cerebral Small Vessel Disease Complicated by Cognitive Impairment

Hengya Zhu  1 , Jingjing Qiu  1 , Xiaoyan Sun  1 , Xiangyan Yang  1 , Bin Zhang  1 , Ying Tan  1
Affiliations

Intelligent Algorithm-Based Quantitative Electroencephalography in Evaluating Cerebral Small Vessel Disease Complicated by Cognitive Impairment

Hengya Zhu et al. Comput Math Methods Med. .

Retraction in

Abstract

To analyze the application value of artificial intelligence model based on Visual Geometry Group- (VGG-) 16 combined with quantitative electroencephalography (QEEG) in cerebral small vessel disease (CSVD) with cognitive impairment, 72 patients with CSVD complicated by cognitive impairment were selected as the research subjects. As per Diagnostic and Statistical Manual (5th Edition), they were divided into the vascular dementia (VD) group of 34 cases and vascular cognitive impairment with no dementia (VCIND) group of 38 cases. The two groups were analyzed for the clinical information, neuropsychological test results, and monitoring results of QEEG based on intelligent algorithms for more than 2 hours. The accuracy rate of VGG was 84.27% and Kappa value was 0.7, while that of modified VGG (nVGG) was 88.76% and Kappa value was 0.78. The improved VGG algorithm obviously had higher accuracy. The test results found that the QEEG identified 8 normal, 19 mild, 10 moderate, and 0 severe cases in the VCIND group, while in the VD group, the corresponding numbers were 4, 13, 11, and 7; in the VCIND group, 7 cases had the normal QEEG, 11 cases had background changes, 9 cases had abnormal waves, and 11 cases had in both background changes and abnormal waves, and in the VD group, the corresponding numbers were 5, 2, 5, and 22, respectively; in the VCIND group, QEEG of 18 patients had no abnormal waves, QEEG of 11 patients had a few abnormal waves, and QEEG of 9 patients had many abnormal waves, and QEEG of 0 people had a large number of abnormal waves, and in the VD group, the corresponding numbers were 7, 6, 12, and 9. The above data were statistically different between the two groups (P < 0.05). Hence, QEEG based on intelligent algorithms can make a good assessment of CSVD with cognitive impairment, which had good clinical application value.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
VGG-16 network model.
Figure 2
Figure 2
Improved VGG network model.
Figure 3
Figure 3
The effect of VGG and nVGG on the results. Note: ∗ indicated that the difference was statistically significant compared with the improved nVGG algorithm (P < 0.05).
Figure 4
Figure 4
Improved VGG identifying EEG of patients in the VD group.
Figure 5
Figure 5
EEG classification of the two groups of patients: (a) the CSVCI group; (b) the VCIND group; (c) the VD group.
Figure 6
Figure 6
The overall changes of the EEG of the two groups of patients: (a) the CSVCI group; (b) the VCIND group; (c) the VD group.
Figure 7
Figure 7
The distribution of abnormal EEG waves in the two groups of patients: (a) the CSVCI group; (b) the VCIND group; (c) the VD group.
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