Abstract：Mild cognitive impairment (MCI) is an important stage of early diagnosis and timely treatment in the course of Alzheimer's disease. Therefore, early detection and early intervention are urgently needed. Aiming at the problems of early diagnosis of MCI, especially the location of sensitive brain areas in early diagnosis, an optimization algorithm of EEG feature extraction based on multi-scale entropy, namely multi-scale entropy feature optimization algorithm, was proposed. The algorithm was able to mine detailed information to the greatest extent by constructing multi-scale sequences and fully considering the contribution of each sequence. During the experiment, we collected the clinical EEG data of 49 subjects, including 28 in the experimental group (MCI group) and 21 in the normal control group. The entropy values of 16 channel multi-scale entropy feature optimization algorithm in MCI group were lower than those in the control group, and there were significant differences in the prefrontal lobe, anterior temporal lobe and middle temporal lobe brain regions (P<0.01). Only this feature was used as the input feature of the classifier to analyze the three brain regions of prefrontal lobe, anterior temporal lobe and middle temporal lobe, and the recognition rates of the brain region diagnosis test set were 83.33%, 86.67% and 73.33% respectively. Further, the AUC values of the two channels of the anterior temporal lobe with the highest recognition rate were calculated to be 0.753 and 0.733, respectively. The results showed that the entropy feature of the multi-scale entropy feature optimization algorithm could fully reflect the changes of EEG signals and could be used as a potential biomarker for the early diagnosis of MCI. The anterior temporal lobe brain region can provide research support for the sensitive brain region to evaluate the state of brain cognitive function in patients with MCI.
杨长杰, 李昕, 侯永捷, 王玉琳, 刘沁爽, 苏芮. 基于多尺度熵特征优化算法的MCI早期诊断及敏感脑区分析[J]. 中国生物医学工程学报, 2023, 42(3): 274-280.
Yang Changjie, Li Xin, Hou Yongjie, Wang Yulin, Liu Qinshuang, Su Rui. Early Diagnosis of MCI and Analysis of Sensitive Brain Regions Based on Multi-Scale Entropy Feature Optimization Algorithm. Chinese Journal of Biomedical Engineering, 2023, 42(3): 274-280.
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