Precise Discrimination for Multiple Etiologies of Dementia Cases Based on Deep Learning with Electroencephalography
Precise Discrimination for Multiple Etiologies of Dementia Cases Based on Deep Learning with Electroencephalography
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DOI：
10.1159/000528439
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发表时间：
2023-01-19
影响因子：
3.2
通讯作者：
Yanagisawa，Takufumi
中科院分区：
文献类型：
--
作者：
Hata，Masahiro;Watanabe，Yusuke;Yanagisawa，Takufumi
前言在全球范围内痴呆患者迅速增加的情况下，开发准确、通用的痴呆疾病生物标志物对妥善应对痴呆问题至关重要。从这个意义上说，脑电图（EEG）已被用作一种有前途的检查，以筛选和辅助诊断痴呆症，具有神经功能敏感，廉价和高可用性的优点。此外，基于算法的深度学习可以扩展EEG的适用性，即使在没有任何研究专家的情况下，也可以轻松地应用于综合医院，从而获得准确和自动的分类。基于该网络，我们分析了健康志愿者的脑电数据（HV，N= 55），阿尔茨海默病患者（AD，N= 101），路易体痴呆（DLB，N= 75）和特发性正常压力脑积水（iNPH，N= 60），以评估这些疾病的判别准确率。分别为81.7%（vs. AD）、93.9%（vs. DLB）、93.1%（vs. iNPH）和87.7%（vs. AD、DLB和iNPH）。结论本研究表明，基于一种新的深度学习算法，可以成功地将痴呆患者的EEG数据与HV区分开来，这可能有助于痴呆疾病的自动筛查和辅助诊断。
IntroductionIt is critical to develop accurate and universally available biomarkers for dementia diseases to appropriately deal with the dementia problems under world-wide rapid increasing of patients with dementia. In this sense, electroencephalography (EEG) has been utilized as a promising examination to screen and assist in diagnosing dementia, with advantages of sensitiveness to neural functions, inexpensiveness, and high availability. Moreover, the algorithm-based deep learning can expand EEG applicability, yielding accurate and automatic classification easily applied even in general hospitals without any research specialist.MethodsWe utilized a novel deep neural network, with which high accuracy of discrimination was archived in neurological disorders in the previous study. Based on this network, we analyzed EEG data of healthy volunteers (HVs, N= 55), patients with Alzheimer’s disease (AD, N= 101), dementia with Lewy bodies (DLB, N= 75), and idiopathic normal pressure hydrocephalus (iNPH, N= 60) to evaluate the discriminative accuracy of these diseases.ResultsHigh discriminative accuracies were archived between HV and patients with dementia, yielding 81.7%(vs. AD), 93.9%(vs. DLB), 93.1%(vs. iNPH), and 87.7%(vs. AD, DLB, and iNPH).ConclusionThis study revealed that the EEG data of patients with dementia were successfully discriminated from HVs based on a novel deep learning algorithm, which could be useful for automatic screening and assisting diagnosis of dementia diseases.