Topological Data Analysis of Single-Trial Electroencephalographic Signals.
Topological Data Analysis of Single-Trial Electroencephalographic Signals.
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DOI：
10.1214/17-aoas1119
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发表时间：
2018-09
期刊：
影响因子：
--
通讯作者：
Chung MK
中科院分区：
文献类型：
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作者：
Wang Y;Ombao H;Chung MK
癫痫是一种神经系统疾病，可对人类大脑的视觉、听觉和运动功能产生负面影响。神经生理记录的统计分析，如脑电图（EEG），有助于理解和诊断癫痫发作。然而，标准的统计方法，不占嵌入在EEG信号的拓扑特征。在目前的研究中，我们提出了一个持续同源（PH）的程序来分析单次试验EEG信号。该程序使用加权傅里叶级数（WFS）对信号进行去噪，并使用基于PH特征持久性景观（PL）的排列测试来测试去噪信号之间的拓扑差异。仿真研究表明，该方法能有效地识别两个信号之间的拓扑差异和不变性。在一个单次试验多通道癫痫发作EEG数据集的应用中，我们提出的PH程序能够识别左颞区始终显示拓扑不变性，这表明癫痫发作期间傅立叶分解的PH特征与癫痫发作前的过程相似。这一发现很重要，因为它不能从EEG数据的简单视觉检查中识别出来，事实上，它被同一数据集的早期分析所遗漏。
Epilepsy is a neurological disorder that can negatively affect the visual, audial and motor functions of the human brain. Statistical analysis of neurophysiological recordings, such as electroencephalogram (EEG), facilitates the understanding and diagnosis of epileptic seizures. Standard statistical methods, however, do not account for topological features embedded in EEG signals. In the current study, we propose a persistent homology (PH) procedure to analyze single-trial EEG signals. The procedure denoises signals with a weighted Fourier series (WFS), and tests for topological difference between the denoised signals with a permutation test based on their PH features persistence landscapes (PL). Simulation studies show that the test effectively identifies topological difference and invariance between two signals. In an application to a single-trial multichannel seizure EEG dataset, our proposed PH procedure was able to identify the left temporal region to consistently show topological invariance, suggesting that the PH features of the Fourier decomposition during seizure is similar to the process before seizure. This finding is important because it could not be identified from a mere visual inspection of the EEG data and was in fact missed by earlier analyses of the same dataset.