Towards automated electroencephalography-based Alzheimer's disease diagnosis using portable low-density devices
Towards automated electroencephalography-based Alzheimer's disease diagnosis using portable low-density devices
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DOI：
10.1016/j.bspc.2016.12.009
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发表时间：
2017-03-01
影响因子：
5.1
通讯作者：
Anghinah, Renato
中科院分区：
文献类型：
--
作者：
Cassani, Raymundo;Falk, Tiago H.;Anghinah, Renato
今天，阿尔茨海默氏病（AD）的诊断是使用主观的精神状态检查，通过稀缺和昂贵的神经成像扫描和侵入性实验室测试辅助研究进行的;所有这些都使得诊断耗时，地理局限性和成本高昂。由于这些局限性，脑电定量分析被认为是一种无创的、更方便的研究AD的技术。已发表的基于EEG的AD诊断的著作通常具有两个主要特征：EEG由经验丰富的临床医生手动选择以丢弃影响AD诊断的伪影，以及依赖具有20个或更多电极的EEG设备。然而，最近的工作表明，通过使用自动伪影去除（AAR）算法结合中密度EEG设置，结果很有希望。然而，在过去的几年里，低密度、便携式EEG设备已经出现，从而为低收入国家和偏远地区（如加拿大北极地区）的低成本AD诊断打开了大门。不幸的是，基于低密度便携式设备的自动诊断解决方案的性能仍然是未知的。本文的工作旨在填补这一空白。我们提出了一个基于AAR和低密度（7通道）EEG设置的自动化基于EEG的AD诊断系统。EEG数据采集期间从控制和AD参与者的休息-清醒协议。AAR后，共同的EEG功能，频谱功率和相干性，计算沿着与最近提出的幅度调制功能。所获得的功能用于训练和测试的建议诊断系统。我们报告和讨论这样的系统所获得的结果，并比较所获得的性能与文献中发表的结果，使用更高密度的EEG布局。(C)2016爱思唯尔有限公司版权所有。
Today, Alzheimer's disease (AD) diagnosis is carried out using subjective mental status examinations assisted in research by scarce and expensive neuroimaging scans and invasive laboratory tests; all of which render the diagnosis time-consuming, geographically confined and costly. Driven by these limitations, quantitative analysis of electroencephalography (EEG) has been proposed as a non-invasive and more convenient technique to study AD. Published works on EEG-based AD diagnosis typically share two main characteristics: EEG is manually selected by experienced clinicians to discard artefacts that affect AD diagnosis, and reliance on EEG devices with 20 or more electrodes. Recent work, however, has suggested promising results by using automated artefact removal (AAR) algorithms combined with medium-density EEG setups. Over the last couple of years, however, low-density, portable EEG devices have emerged, thus opening the doors for low-cost AD diagnosis in low-income countries and remote regions, such as the Canadian Arctic. Unfortunately, the performance of automated diagnostic solutions based on low-density portable devices is still unknown. The work presented here aims to fill this gap. We propose an automated EEG-based AD diagnosis system based on AAR and a low-density (7- channel) EEG setup. EEG data was acquired during resting-awake protocol from control and AD participants. After AAR, common EEG features, spectral power and coherence, are computed along with the recently proposed amplitude-modulation features. The obtained features are used for training and testing of the proposed diagnosis system. We report and discuss the results obtained with such system and compare the obtained performance with results published in the literature using higher-density EEG layouts. (C) 2016 Elsevier Ltd. All rights reserved.