基于智能算法的定量脑电图评估伴有认知障碍的脑小血管病。
Intelligent Algorithm-Based Quantitative Electroencephalography in Evaluating Cerebral Small Vessel Disease Complicated by Cognitive Impairment.
机构信息
Department of Neurology, Huzhou Center Hospital, Affiliated Center Hospital of Huzhou University, No. 1558 Sanhuan North Road, Huzhou, 313000 Zhejiang, China.
出版信息
Comput Math Methods Med. 2022 Jan 29;2022:9398551. doi: 10.1155/2022/9398551. eCollection 2022.
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 (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 ( < 0.05). Hence, QEEG based on intelligent algorithms can make a good assessment of CSVD with cognitive impairment, which had good clinical application value.
为分析基于视觉几何组-16（VGG-16）的人工智能模型与定量脑电图（QEEG）在伴有认知障碍的脑小血管病（CSVD）中的应用价值，选取 72 例伴有认知障碍的 CSVD 患者为研究对象。依据（第 5 版）将其分为血管性痴呆（VD）组 34 例和血管性认知障碍无痴呆（VCIND）组 38 例。对两组的临床资料、神经心理学测试结果、基于智能算法的 QEEG 监测结果进行分析，监测时长均超过 2 小时。VGG 准确率为 84.27%，Kappa 值为 0.7，改良 VGG（nVGG）准确率为 88.76%，Kappa 值为 0.78。改进后的 VGG 算法准确率明显更高。检测结果发现 VCIND 组 QEEG 正常 8 例、轻度异常 19 例、中度异常 10 例、重度异常 0 例，VD 组分别为 4 例、13 例、11 例、7 例；VCIND 组 QEEG 正常 7 例、背景改变 11 例、异常波 9 例、背景改变和异常波均有 11 例，VD 组分别为 5 例、2 例、5 例、22 例；VCIND 组 QEEG 无异常波 18 例、少数异常波 11 例、多数异常波 9 例、大量异常波 0 例，VD 组分别为 7 例、6 例、12 例、9 例。两组上述数据比较差异均有统计学意义（ < 0.05）。由此可见，基于智能算法的 QEEG 可对伴有认知障碍的 CSVD 做出较好评估，具有良好的临床应用价值。
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