SPARSE ANOMALY REPRESENTATIONS IN VERY HIGH-DIMENSIONAL BRAIN SIGNALS
SPARSE ANOMALY REPRESENTATIONS IN VERY HIGH-DIMENSIONAL BRAIN SIGNALS
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DOI：
10.1109/dsw.2018.8439924
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发表时间：
2018-06
期刊：
影响因子：
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通讯作者：
C. Stamoulis
中科院分区：
文献类型：
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作者：
C. Stamoulis
在组织的各个层面上，大脑活动本质上是稀疏的。与神经系统疾病（如癫痫）相关的短暂性信号异常也是如此。因此，为了表征这些异常，非常高维的大脑信号可能被表示为一个全面（过完整）字典元素的稀疏组合。这样的字典可以从感兴趣的数据集中估计（学习）。然而，考虑到长时间记录的大脑信号的统计、频谱和特征的异质性，词典的大小可能不是最理想的，特别是就其大小而言。本文探讨了通过K-SVD算法从连续数天收集的无创高频脑信号中估计出的信号特异性、数据集特异性和个体特异性异常字典。研究表明，信号特定字典可能产生比通过组合来自多个电极的训练信号估计的更准确的表示。
Across scales of organization, brain activity is inherently sparse. This is also the case for transiently occurring signal abnormalities associated with neurological disorders, such as epilepsy. Consequently, for the purpose of characterizing these abnormalities, very high-dimensional brain signals may be represented as sparse combinations of the elements of a comprehensive (overcomplete) dictionary. Such a dictionary may be estimated (learned) from the dataset(s) of interest. However, given the statistical, spectral and signature heterogeneity of brain signals recorded over long periods of times, the size of the dictionary may be suboptimal, particularly in terms of its size. In this paper, signal-specific, dataset-specific and individual-specific anomaly dictionaries, estimated via the K-SVD algorithm from noninvasive high-frequency brain signals collected continuously over several days are explored. It is shown that signal-specific dictionaries may yield substantially more accurate representations than those estimated by combining training signals from multiple electrodes.