A Spectral Clustering Approach for the Classification of Waveform Anomalies in High-Dimensional Brain Signals
A Spectral Clustering Approach for the Classification of Waveform Anomalies in High-Dimensional Brain Signals
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DOI：
10.1109/embc44109.2020.9176369
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发表时间：
2020-07
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
人类大脑中的瞬时电生理异常与神经系统疾病（如癫痫）相关，可能预示着即将发生的不良事件（如癫痫），或可能反映了应激源（如睡眠不足）的影响。这些，通常是短暂的，高频和异构的信号异常仍然知之甚少，特别是在长时间尺度上，其形态和变异性还没有得到系统的特点。在连续的神经记录中，其固有的稀疏性、短持续时间和低幅度使得其检测和分类困难。反过来，这限制了它们作为异常神经动力学过程的潜在生物标志物的评价（例如，发病）和即将发生的不良事件的预测因子。提出了一种新的算法，利用固有的稀疏性记录在头皮上的神经信号中的高频异常，并使用谱聚类将它们分类在非常高维的信号跨越几天。结果表明，估计集群随时间动态变化，其分布变化基本上都作为时间和空间的函数。
Transient electrophysiological anomalies in the human brain have been associated with neurological disorders such as epilepsy, may signal impending adverse events (e.g, seizurse), or may reflect the effects of a stressor, such as insufficient sleep. These, typically brief, high-frequency and heterogeneous signal anomalies remain poorly understood, particularly at long time scales, and their morphology and variability have not been systematically characterized. In continuous neural recordings, their inherent sparsity, short duration and low amplitude makes their detection and classification difficult. In turn, this limits their evaluation as potential biomarkers of abnormal neurodynamic processes (e.g., ictogenesis) and predictors of impending adverse events. A novel algorithm is presented that leverages the inherent sparsity of high-frequency abnormalities in neural signals recorded at the scalp and uses spectral clustering to classify them in very high-dimensional signals spanning several days. It is shown that estimated clusters vary dynamically with time and their distribution changes substantially both as a function of time and space.