在临床上很难将路易体痴呆与其他常见痴呆类型区分开来,相当多的病例只有在死后才被发现。因此,显然需要廉价和准确的临床诊断方法.脑电图(EEG)由于其相对较低的成本和非侵入性而成为一种潜在的候选者。以前检查使用EEG作为痴呆诊断的研究集中在闭眼(EC)静息状态;然而,由于临床可用性,睁眼(EO)EEG也可能是定量分析的有用辅助手段。
我们从研究研究协议下记录的EEG信号中提取频谱特性(1024Hz采样率,10:5EEG布局)。数据来自40名痴呆症患者,平均年龄分别为74.42岁、75.81岁和73.88岁。路易体痴呆(DLB)和帕金森病痴呆(PDD),分别,和15名健康对照(HC),平均年龄为76.93岁。我们利用k最近邻,支持向量机和逻辑回归机器学习利用来自三角洲的光谱数据来区分群体,theta,高θ,α和β脑电图带。
我们发现,与不使用EO数据的方法相比,EC和EO静息状态EEG数据的组合显着提高了组间分类准确性。其次,我们观察到EO和EC状态之间HC的主导频率方差明显增加,在任何痴呆亚组中均未观察到。对于组间分类,我们对HC与痴呆分类的特异性为0.87,敏感性为0.92,对AD与DLB分类的特异性为0.75,敏感性为0.91,k最近邻机器学习模型优于其他机器学习方法。
我们的研究结果表明,在对老年人的痴呆类型进行分类时,EC和EO定量EEG特征的结合提高了总体分类准确性。此外,我们证明,健康对照显示EC和EO状态之间的主导频率方差有明确的变化。在未来,应使用验证队列进一步巩固这些发现.
The differentiation of Lewy body dementia from other common dementia types clinically is difficult, with a considerable number of cases only being found post-mortem. Consequently, there is a clear need for inexpensive and accurate diagnostic approaches for clinical use. Electroencephalography (EEG) is one potential candidate due to its relatively low cost and non-invasive nature. Previous studies examining the use of EEG as a dementia diagnostic have focussed on the eyes closed (EC) resting state; however, eyes open (EO) EEG may also be a useful adjunct to quantitative analysis due to clinical availability.
We extracted spectral properties from EEG signals recorded under research study protocols (1024 Hz sampling rate, 10:5 EEG layout). The data stems from a total of 40 dementia patients with an average age of 74.42, 75.81 and 73.88 years for Alzheimer\'s disease (AD), dementia with Lewy bodies (DLB) and Parkinson\'s disease dementia (PDD), respectively, and 15 healthy controls (HC) with an average age of 76.93 years. We utilised k-nearest neighbour, support vector machine and logistic regression machine learning to differentiate between groups utilising spectral data from the delta, theta, high theta, alpha and beta EEG bands.
We found that the combination of EC and EO resting state EEG data significantly increased inter-group classification accuracy compared to methods not using EO data. Secondly, we observed a distinct increase in the dominant frequency variance for HC between the EO and EC state, which was not observed within any dementia subgroup. For inter-group classification, we achieved a specificity of 0.87 and sensitivity of 0.92 for HC vs dementia classification and 0.75 specificity and 0.91 sensitivity for AD vs DLB classification, with a k-nearest neighbour machine learning model which outperformed other machine learning methods.
The findings of our study indicate that the combination of both EC and EO quantitative EEG features improves overall classification accuracy when classifying dementia types in older age adults. In addition, we demonstrate that healthy controls display a definite change in dominant frequency variance between the EC and EO state. In future, a validation cohort should be utilised to further solidify these findings.