The effects of automated artifact removal algorithms on electroencephalography-based Alzheimer's disease diagnosis.
The effects of automated artifact removal algorithms on electroencephalography-based Alzheimer's disease diagnosis.
复制标题
DOI：
10.3389/fnagi.2014.00055
复制
发表时间：
2014
影响因子：
4.8
通讯作者：
Anghinah R
中科院分区：
文献类型：
--
作者：
Cassani R;Falk TH;Fraga FJ;Kanda PA;Anghinah R
在过去的十年中，脑电图（EEG）已成为一种可靠的工具，用于诊断皮质疾病，如阿尔茨海默病（AD）。然而，EEG信号易受几种伪影的影响，例如眼睛、肌肉、运动和环境。为了克服这种限制，现有的诊断系统通常依赖于有经验的临床医生从收集的多通道EEG数据中手动选择无伪影的时期。然而，手动选择是一个繁琐且耗时的过程，使得诊断系统“半自动化”。尽管如此，在文献中已经提出了许多EEG伪影去除算法。然而，在自动AD诊断系统中使用这种算法的（缺点）优点还没有被记录;本文旨在填补这一空白。在这里，我们研究了三种最先进的自动伪影去除（AAR）算法（单独和相互结合）对AD诊断系统的影响，这些算法基于四种不同类别的EEG特征，即频谱、幅度调制变化率、相干性和相位。测试的三种AAR算法是统计伪影抑制（SAR），基于二阶盲识别和典型相关分析的盲源分离（BSS-SOBI-CCA），和小波增强独立分量分析（wICA）。基于从59名参与者收集的20通道静息-清醒EEG数据的实验结果（20例轻度AD患者，15例中度至重度AD患者和24例年龄匹配的健康对照）显示，在三项任务中，单独使用wICA算法优于其他增强算法组合：诊断（对照组vs.轻度vs.中度），早期检测（对照vs.轻度）和疾病进展（轻度与中度），从而为全自动系统打开了大门，可以帮助临床医生早期发现AD，以及疾病严重程度进展评估。
Over the last decade, electroencephalography (EEG) has emerged as a reliable tool for the diagnosis of cortical disorders such as Alzheimer's disease (AD). EEG signals, however, are susceptible to several artifacts, such as ocular, muscular, movement, and environmental. To overcome this limitation, existing diagnostic systems commonly depend on experienced clinicians to manually select artifact-free epochs from the collected multi-channel EEG data. Manual selection, however, is a tedious and time-consuming process, rendering the diagnostic system “semi-automated.” Notwithstanding, a number of EEG artifact removal algorithms have been proposed in the literature. The (dis)advantages of using such algorithms in automated AD diagnostic systems, however, have not been documented; this paper aims to fill this gap. Here, we investigate the effects of three state-of-the-art automated artifact removal (AAR) algorithms (both alone and in combination with each other) on AD diagnostic systems based on four different classes of EEG features, namely, spectral, amplitude modulation rate of change, coherence, and phase. The three AAR algorithms tested are statistical artifact rejection (SAR), blind source separation based on second order blind identification and canonical correlation analysis (BSS-SOBI-CCA), and wavelet enhanced independent component analysis (wICA). Experimental results based on 20-channel resting-awake EEG data collected from 59 participants (20 patients with mild AD, 15 with moderate-to-severe AD, and 24 age-matched healthy controls) showed the wICA algorithm alone outperforming other enhancement algorithm combinations across three tasks: diagnosis (control vs. mild vs. moderate), early detection (control vs. mild), and disease progression (mild vs. moderate), thus opening the doors for fully-automated systems that can assist clinicians with early detection of AD, as well as disease severity progression assessment.