Automatic Artifact Removal in EEG of Normal and Demented Individuals Using ICA-WT during Working Memory Tasks.
Automatic Artifact Removal in EEG of Normal and Demented Individuals Using ICA-WT during Working Memory Tasks.
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DOI：
10.3390/s17061326
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发表时间：
2017-06-08
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影响因子：
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通讯作者：
Escudero J
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作者：
Al-Qazzaz NK;Hamid Bin Mohd Ali S;Ahmad SA;Islam MS;Escudero J
描述痴呆症的特征是支持个性化医疗保健的全球挑战。脑电图（EEG）是支持诊断和评估人脑异常的有前途的工具。脑电图传感器以出色的时间分辨率直接记录大脑活动。在这项研究中，使用带有 19 个电极的脑电图传感器来测试 5 名血管性痴呆（VaD）、15 名中风相关的轻度认知障碍（MCI）患者和 15 名健康受试者在工作记忆（WM）任务期间的大脑背景活动。这项研究的目的是双重的。首先，它的目的是使用自动独立成分分析（AICA）和小波变换（WT）相结合的新技术，即AICA-WT技术来增强记录的EEG信号；其次，它的目的是提取和研究中风后痴呆患者与对照受试者相比的光谱特征。所提出的 AICA-WT 技术是一种四阶段方法。在第一阶段，估计独立组件（IC）。在第二阶段，应用三步工件识别指标来检测工件组件。在第三阶段，被识别为伪影的组件被标记为关键组件并通过 DWT 进行去噪。在第四阶段，重建校正后的IC以获得无伪影的EEG信号。使用互相关和峰值信噪比（ANOVA，˂ 0.05）将所提出的 AICA-WT 技术的性能与基于 AICA 和 WT 去噪方法的其他两种技术进行比较。 AICA-WT 技术表现出最佳的伪影去除性能。使用 AICA-WT（ANOVA，˂ 0.05）评估 VaD 和 MCI 患者的 EEG 主导频率与对照受试者相比有所减慢的假设。因此，本研究可以通过脑电图背景活动的频谱分析，提供有关中风后痴呆，特别是 VaD 和中风相关 MCI 患者的信息，这有助于利用脑电图信号处理提供有用的诊断指标。
Characterizing dementia is a global challenge in supporting personalized health care. The electroencephalogram (EEG) is a promising tool to support the diagnosis and evaluation of abnormalities in the human brain. The EEG sensors record the brain activity directly with excellent time resolution. In this study, EEG sensor with 19 electrodes were used to test the background activities of the brains of five vascular dementia (VaD), 15 stroke-related patients with mild cognitive impairment (MCI), and 15 healthy subjects during a working memory (WM) task. The objective of this study is twofold. First, it aims to enhance the recorded EEG signals using a novel technique that combines automatic independent component analysis (AICA) and wavelet transform (WT), that is, the AICA–WT technique; second, it aims to extract and investigate the spectral features that characterize the post-stroke dementia patients compared to the control subjects. The proposed AICA–WT technique is a four-stage approach. In the first stage, the independent components (ICs) were estimated. In the second stage, three-step artifact identification metrics were applied to detect the artifactual components. The components identified as artifacts were marked as critical and denoised through DWT in the third stage. In the fourth stage, the corrected ICs were reconstructed to obtain artifact-free EEG signals. The performance of the proposed AICA–WT technique was compared with those of two other techniques based on AICA and WT denoising methods using cross-correlation and peak signal to noise ratio (ANOVA, ˂ 0.05). The AICA–WT technique exhibited the best artifact removal performance. The assumption that there would be a deceleration of EEG dominant frequencies in VaD and MCI patients compared with control subjects was assessed with AICA–WT (ANOVA, ˂ 0.05). Therefore, this study may provide information on post-stroke dementia particularly VaD and stroke-related MCI patients through spectral analysis of EEG background activities that can help to provide useful diagnostic indexes by using EEG signal processing.