Improving the Quality of EEG Data in Patients with Alzheimer's Disease Using ICA
Improving the Quality of EEG Data in Patients with Alzheimer's Disease Using ICA
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DOI：
10.1007/978-3-642-03040-6_119
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发表时间：
2009-07
期刊：
影响因子：
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通讯作者：
F. Vialatte;Jordi Solé-Casals;M. Maurice;C. Latchoumane;N. Hudson;S. Wimalaratna;Jaeseung Jeong;A. Cichocki
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文献类型：
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作者：
F. Vialatte;Jordi Solé-Casals;M. Maurice;C. Latchoumane;N. Hudson;S. Wimalaratna;Jaeseung Jeong;A. Cichocki
独立成分分析（伊卡）是否会使EEG信号变性？我们将伊卡应用于两组受试者（轻度阿尔茨海默病患者和对照组）。本研究的目的是检查伊卡方法是否可以减少组间差异和受试者内变异性。我们发现伊卡降低了验证的留一法均方根误差（RMSE）（从0.32降低到0.28），表明组间差异减小。更有趣的是，伊卡降低了每组内的受试者间变异性（伊卡前δ范围内的σ= 2.54，ICA后σ= 1.56，Bonferroni校正后Bartlett p = 0.046）。此外，我们提出了一种方法，以限制伊卡清洗过程中人为错误的影响（13.8%，75.6%的清洗器间一致性），并减少人为偏见。这些发现表明伊卡在阿尔茨海默病的临床EEG中用于减少受试者变异性的新用途。
Does Independent Component Analysis (ICA) denature EEG signals? We applied ICA to two groups of subjects (mild Alzheimer patients and control subjects). The aim of this study was to examine whether or not the ICA method can reduce both group differences and within-subject variability. We found that ICA diminished Leave-One-Out root mean square error (RMSE) of validation (from 0.32 to 0.28), indicative of the reduction of group difference. More interestingly, ICA reduced the inter-subject variability within each group (σ= 2.54 in theδrange before ICA,σ= 1.56 after, Bartlett p = 0.046 after Bonferroni correction). Additionally, we present a method to limit the impact of human error (≃ 13.8%, with 75.6% inter-cleaner agreement) during ICA cleaning, and reduce human bias. These findings suggests the novel usefulness of ICA in clinical EEG in Alzheimer’s disease for reduction of subject variability.