结合脑电图和磁共振成像的非线性特征诊断阿尔茨海默病

Q1 Decision Sciences
Elias Mazrooei Rad, Mahdi Azarnoosh, Majid Ghoshuni, Mohammad Mahdi Khalilzadeh
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引用次数: 0

摘要

本文提出了一种结合脑电信号和MRI图像特征诊断轻度阿尔茨海默病的新方法。记录90名健康受试者、轻度和重度阿尔茨海默病(AD)患者的Pz、Cz、Fz三个通道的闭眼、睁眼、提醒和刺激四种模式下的脑信号。此外,MRI图像至少为3特斯拉,厚度为3毫米,可以检查老年斑和神经原纤维缠结。适当的图像分割,掩码和锐利的过滤器用于预处理。然后根据大脑的非线性和混沌特性提取相应的特征,如Lyapunov指数、相关维数、熵等。结果:结合内侧颞叶萎缩(MTA)、脑脊液(CSF)、灰质(GM)、指数不对称(IA)、白质(WM)等脑MRI图像特征诊断本病。然后使用支持向量机和Elman神经网络两种分类器,通过方差分析提取最优组合特征。结果表明,在三种脑信号之间,以及在四种评估模式之间,Pz通道和激励模式的准确性高于其他模式。结论:最后,由于所研究的神经网络动力学的非线性特性和脑电信号的非线性动力学特性,采用了Elman神经网络。然而,重要的是要注意,通过分析医学图像的方式,我们可以确定记录大脑信号的最有效通道。MRI图像的三维分割进一步帮助研究人员诊断阿尔茨海默病并获得重要信息。在脑信号特征与医学图像相结合的情况下,Elman神经网络结果的准确率为94.4%,在不结合信号与图像特征的情况下,结果的准确率为92.2%。由于脑信号的非线性动态,非线性分类器的使用比其他分类方法更合适。在脑信号特征与医学图像相结合的情况下,以RBF为核心的支持向量机结果的准确率为75.5%,在不结合信号与图像特征的情况下,结果的准确率为76.8%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Combining Nonlinear Features of EEG and MRI to Diagnose Alzheimer’s Disease

Combining Nonlinear Features of EEG and MRI to Diagnose Alzheimer’s Disease

This article, a new method for the diagnosis of Alzheimer’s disease in the mild stage is presented according to combining the characteristics of EEG signal and MRI images. The brain signal is recorded in four modes of closed-eyes, open eye, reminder, and stimulation from three channels Pz, Cz, and Fz of 90 participants in three groups of healthy subjects, mild, and severe Alzheimer’s disease (AD) patients.In addition, MRI images are taken with at least 3 Tesla and the thickness of 3 mm so it can be examined the senile plaques and neurofibrillary tangles. Proper image segmentation, mask, and sharp filters are used for preprocessing. Then proper features of brain signals extracted according to the nonlinear and chaotic nature of the brain such as Lyapunov exponent, correlation dimension, and entropy. Results: These features combined with brain MRI images properties including Medial Temporal Lobe Atrophy (MTA), Cerebral Spinal Fluid (CSF), Gray Matter (GM), Index Asymmetry (IA) and White Matter (WM) to diagnose the disease. Then two classifiers, the support vector machine, and Elman neural network are used with the optimal combined features extracted by analysis of variance. Results showed that between the three brain signals, and between the four modes of evaluation, the accuracy of the Pz channel and excitation mode was more than the others. Conclusions: Finally, by using neural network dynamics because of the nonlinear properties studied and due to the nonlinear dynamics of the EEG signal, the Elman neural network is used. However, it is the important to note that, by the way of analyzing medical images, we can determine the most effective channel for recording brain signals. 3D segmentation of MRI images further helps researchers diagnose Alzheimer’s disease and obtain important information. The accuracy of the results in Elman neural network with the combination of brain signal features and medical images is 94.4% and in the case without combining the signal and image features, the accuracy of the results is 92.2%. The use of nonlinear classifiers is more appropriate than other classification methods due to the nonlinear dynamics of the brain signal. The accuracy of the results in the support vector machine with RBF core with the combination of brain signal features and medical images is 75.5% and in the case without combining the signal and image features, the accuracy of the results is 76.8%.

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来源期刊
Annals of Data Science
Annals of Data Science Decision Sciences-Statistics, Probability and Uncertainty
CiteScore
6.50
自引率
0.00%
发文量
93
期刊介绍: Annals of Data Science (ADS) publishes cutting-edge research findings, experimental results and case studies of data science. Although Data Science is regarded as an interdisciplinary field of using mathematics, statistics, databases, data mining, high-performance computing, knowledge management and virtualization to discover knowledge from Big Data, it should have its own scientific contents, such as axioms, laws and rules, which are fundamentally important for experts in different fields to explore their own interests from Big Data. ADS encourages contributors to address such challenging problems at this exchange platform. At present, how to discover knowledge from heterogeneous data under Big Data environment needs to be addressed.     ADS is a series of volumes edited by either the editorial office or guest editors. Guest editors will be responsible for call-for-papers and the review process for high-quality contributions in their volumes.
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