电子测量与仪器学报

李昕,屈中杰,李梓澎,尹立勇,苏芮.个体化频带滑动窗特征的轻度认知障碍诊断研究[J].电子测量与仪器学报,2024,38(2):182-189

个体化频带滑动窗特征的轻度认知障碍诊断研究

Diagnostic study of mild cognitive impairment with individualizedfrequency band sliding window features

DOI：

英文关键词:sliding windows zero model continuous wavelet transform phase lag index

基金项目:河北省自然科学基金 (F2022203005, F2019203515)、燕山大学与秦皇岛市第一医院医务人员交叉特色项目 (UY202201)、河北省科技计划项目(236Z2004G)、河北省教育厅科学研究项目（QN2024061)资助

作者	单位
李昕	1.燕山大学电气工程学院秦皇岛066004；2.河北省测量技术与仪器重点实验室秦皇岛066004；
屈中杰	1.燕山大学电气工程学院秦皇岛066004；2.河北省测量技术与仪器重点实验室秦皇岛066004；
李梓澎	1.燕山大学电气工程学院秦皇岛066004；2.河北省测量技术与仪器重点实验室秦皇岛066004；
尹立勇	河北省秦皇岛市第一医院神经内科秦皇岛066004
苏芮	河北医科大学医学影像学院石家庄050000

Author	Institution
Li Xin	1.School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China; 2.Hebei Provincial Key Laboratory of Measurement Technology and Instrumentation, Qinhuangdao 066004, China;
Qu Zhongjie	1.School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China; 2.Hebei Provincial Key Laboratory of Measurement Technology and Instrumentation, Qinhuangdao 066004, China;
Li Zipeng	1.School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China; 2.Hebei Provincial Key Laboratory of Measurement Technology and Instrumentation, Qinhuangdao 066004, China;
Yin Liyong	Department of Neurology, Qinhuangdao First Hospital, Qinhuangdao 066004, China
Su Rui	School of Medical Imaging, Hebei Medical Univerity, Shijiazhuang 050000, China

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中文摘要:

轻度认知障碍(MCI)是老年性痴呆诊断的关键阶段,脑电(EEG)信号特征可以反映MCI患者的认知状态,帮助实现早期诊断。现有研究在EEG特征提取过程中,针对脑电各节律,大多采用固定的时间窗完成分段处理,忽略了不同节律的特征差异,从而影响诊断效果。针对该问题,本文提出了一种新的组合滑动窗优化算法,该算法通过迭代振幅调整傅里叶变换(IAAFT)对零模型的构建方法进行了改进,以此得到评估大脑动态特性指标KPLI,通过对EEG各频段信号采取多种滑动窗组合,并以KPLI指标引导,得到适合不同频段的最佳滑动窗组合。在最佳滑动窗组合基础上,对各频段组合提取相位滞后指数(PLI),进行连续小波变换(CWT)特征,通过ResNet MLP双通道分类网络实现MCI诊断。结果显示,使用个性化组合频段滑动窗对88名受试者(32名MCI患者,36名阿尔茨海默症患者以及20名正常对照组)实现了诊断分类,得到了82.2%的分类准确率,比固定窗的分类提高了10%(得到了72.2%的分类准确率)。结果表明,基于个体化脑电节律特征组合能够更好提取MCI的特征,提高轻度认知障碍诊断的正确率与特异性,是一种有效的脑电特征提取方法。

英文摘要:

Mild cognitive impairment (MCI) is a key stage in the diagnosis of senile dementia, and the characteristics of electrical brain (EEG) signals can reflect the cognitive status of MCI patients and help achieve early diagnosis. In the process of EEG feature extraction, most existing studies use fixed time windows to complete segmentation processing for each rhythm of EEG, ignoring the feature differences of different rhythms, thus affecting the diagnostic effect. In view of this problem, a new combined sliding window optimization algorithm is proposed, which improves the construction method of zero model by iterative amplitude adjustment Fourier transform (IAAFT), so as to evaluate the brain dynamic characteristics KPLI. By adopting a variety of sliding window combinations for EEG frequency band signals and guiding them with KPLI indicators, the best sliding window combinations suitable for different frequency bands are obtained. Based on the best sliding window combination, the phase lag index (PLI) is extracted from each band combination, the continuous wavelet transform (CWT) feature is performed, and the MCI diagnosis is realized through the ResNet-MLP dual-channel classification network. The results show that diagnostic classification was achieved for 88 subjects (32 MCI patients, 36 Alzheimer’s disease patients, and 20 healthy control) using a personalized combination band sliding window, and the classification accuracy was 82.2%, which is 10% higher than the classification of fixed window (72.2% classification accuracy is obtained). The results showed that based on the individualized EEG rhythm feature combination, the features of MCI could be better extracted, and the accuracy and specificity of the diagnosis of mild cognitive impairment could be improved, which was an effective EEG feature extraction method.

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