Predictive Diagnostic Approach to Dementia and Dementia Subtypes Using Wireless and Mobile Electroencephalography: A Pilot Study
Predictive Diagnostic Approach to Dementia and Dementia Subtypes Using Wireless and Mobile Electroencephalography: A Pilot Study
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使用无线和移动脑电图对痴呆症和痴呆亚型进行预测诊断的方法：一项试点研究
DOI：
10.1089/bioe.2021.0030
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发表时间：
2022
期刊：
影响因子：
2.3
通讯作者：
Takahashi Ryosuke
中科院分区：
文献类型：
--
作者：
Li Fangzhou;Matsumori Shoya;Egawa Naohiro;Yoshimoto Shusuke;Yamashiro Kotaro;Mizutani Haruo;Uchida Noriko;Kokuryu Atsuko;Kuzuya Akira;Kojima Ryosuke;Hayashi Yu;Takahashi Ryosuke
背景：开发老年人群中轻度认知功能障碍的筛查方法，并基于这种方法对痴呆的进展进行早期干预，仍然具有挑战性。脑电图（EEG）是一种非侵入性和敏感的工具，以评估大脑的功能活动，无线和移动的EEG（wmEEG）可以作为一种替代的筛选技术，是广泛耐受的痴呆患者从临床前到严重阶段。材料和方法：使用wmEEG，我们记录了生物电活动（BA）从101个个体的痴呆和非痴呆对照（NC）在4个任务，并探讨了任务可以区分痴呆和NC。结果：我们发现，在三个功率谱的时间-频率分析（3-4，5-7，和17-23 Hz）之间的痴呆和NC之间的睁眼条件下的显着差异和一个显着的一致性差异，在特定的慢α功率谱（6-8 Hz）之间的阿尔茨海默病（AD）和路易体痴呆（DLB）之间的闭眼条件。这些结果通过使用基于整个wmEEG数据集的深度学习方法进行分类分析得到证实，其中在睁眼条件下区分痴呆与NC的准确率高于闭眼条件下的准确率（分别为0.71 vs. 0.52）。结论：wmEEG可作为记录BA的有效工具，分析BA有助于早期痴呆的诊断和痴呆亚型的有效、客观判别。
Background:Developing a screening method for mild cognitive impairment in the aging population and intervening early in the progression of dementia based on such a method, remains challenging. Electroencephalography (EEG) is a noninvasive and sensitive tool to assess the functional activity of the brain, and wireless and mobile EEG (wmEEG) could serve as an alternative screening technique that is widely tolerable in patients with dementia from the preclinical to severe stage.Materials and Methods:Using wmEEG, we recorded bioelectrical activity (BA) from the forehead in 101 individuals with dementia and nondementia controls (NCs) during 4 tasks and investigated which task could differentiate dementia from NC.Results:We found significant differences in three power spectra of the time–frequency analysis (3–4, 5–7, and 17–23 Hz) between dementia and NC under an eyes-open condition and a significant consistent difference in a specific slow alpha power spectrum (6–8 Hz) between Alzheimer's disease (AD) and dementia with Lewy bodies (DLB) under an eyes-closed condition. These results were confirmed by classification analysis using a deep learning method based on the whole wmEEG data sets, in which the accuracy of discriminating dementia from NC under the eyes-open condition was higher than that under the eyes-closed condition (0.71 vs. 0.52, respectively). Moreover, the accuracy of discriminating AD from DLB under the eyes-closed condition was higher than that under the eyes-open condition (0.77 vs. 0.64, respectively).Conclusion:The result of this pilot study suggests that wmEEG can be a useful tool for recording BA, and that analyzing BA may help to detect early dementia and discriminate dementia subtypes effectively and objectively.
影响因子：
4.7
作者：
Lee, Hennie;Brekelmans, Geert J. F.;Roks, Gerwin
通讯作者：
Roks, Gerwin
影响因子：
2
作者：
Fonseca, L. C.;Tedrus, G. M. A. S.;Bossoni, A. S.
通讯作者：
Bossoni, A. S.