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A New Framework for Automatic Detection of Patients With Mild Cognitive Impairment Using Resting-State EEG Signals
IEEE Transactions on Neural Systems and Rehabilitation Engineering ( IF 4.9 ) Pub Date : 2020-07-31 , DOI: 10.1109/tnsre.2020.3013429
Siuly Siuly , Omer Faruk Alcin , Enamul Kabir , Abdulkadir Sengur , Hua Wang , Yanchun Zhang , Frank Whittaker

Mild cognitive impairment (MCI) can be an indicator representing the early stage of Alzheimier’s disease (AD). AD, which is the most common form of dementia, is a major public health problem worldwide. Efficient detection of MCI is essential to identify the risks of AD and dementia. Currently Electroencephalography (EEG) is the most popular tool to investigate the presenence of MCI biomarkers. This study aims to develop a new framework that can use EEG data to automatically distinguish MCI patients from healthy control subjects. The proposed framework consists of noise removal (baseline drift and power line interference noises), segmentation, data compression, feature extraction, classification, and performance evaluation. This study introduces Piecewise Aggregate Approximation (PAA) for compressing massive volumes of EEG data for reliable analysis. Permutation entropy (PE) and auto-regressive (AR) model features are investigated to explore whether the changes in EEG signals can effectively distinguish MCI from healthy control subjects. Finally, three models are developed based on three modern machine learning techniques: Extreme Learning Machine (ELM); Support Vector Machine (SVM) and K-Nearest Neighbours (KNN) for the obtained feature sets. Our developed models are tested on a publicly available MCI EEG database and the robustness of our models is evaluated by using a 10-fold cross validation method. The results show that the proposed ELM based method achieves the highest classification accuracy (98.78%) with lower execution time (0.281 seconds) and also outperforms the existing methods. The experimental results suggest that our proposed framework could provide a robust biomarker for efficient detection of MCI patients.

中文翻译:

利用静息状态脑电信号自动检测轻度认知障碍患者的新框架

轻度认知障碍(MCI)可以代表阿尔茨海默氏病(AD)的早期阶段。AD是痴呆的最常见形式,是全世界主要的公共卫生问题。有效检测MCI对于识别AD和痴呆的风险至关重要。目前,脑电图(EEG)是研究MCI生物标志物存在的最流行工具。这项研究旨在开发一个可以使用EEG数据自动将MCI患者与健康对照对象区分开的新框架。拟议的框架包括噪声消除(基线漂移和电源线干扰噪声),分段,数据压缩,特征提取,分类和性能评估。本研究介绍了分段聚合近似(PAA),用于压缩大量EEG数据以进行可靠的分析。研究了置换熵(PE)和自回归(AR)模型的功能,以探索EEG信号的变化是否可以有效区分MCI和健康对照对象。最后,基于三种现代机器学习技术开发了三种模型:极限学习机器(ELM);支持的矢量机(SVM)和K最近邻(KNN)获得的功能集。我们开发的模型在公共MCI EEG数据库上进行了测试,并且模型的稳健性通过使用10倍交叉验证方法进行了评估。结果表明,所提出的基于ELM的方法以较低的执行时间(0.)达到了最高的分类精度(98.78%)。281秒),并且性能也超过了现有方法。实验结果表明,我们提出的框架可以为有效检测MCI患者提供强大的生物标志物。
更新日期:2020-09-08
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