论文标题
实时检测癫痫发作的拓扑生物标志物
Topological biomarkers for real-time detection of epileptic seizures
论文作者
论文摘要
实时癫痫发作检测是计算神经科学对诊断和治疗改善癫痫病的基本问题。我们提出了一种实时计算方法，用于跟踪和检测原始神经生理记录的癫痫发作。我们的机制基于对从同时记录的通道得出的时间序列的滑动窗口嵌入的拓扑分析。我们通过计算随时间不断发展的拓扑空间的持续同源性来从信号中提取拓扑生物标志物。值得注意的是，拟议的生物标志物强烈捕捉了发作状态期间大脑动力学的变化。我们将我们的方法应用于不同类型的信号，包括头皮和颅内脑电图以及磁脑训练术，在间歇性和发作状态下的患者中，在一系列临床情况下显示出很高的精度。
Real time seizure detection is a fundamental problem in computational neuroscience towards diagnosis and treatment's improvement of epileptic disease. We propose a real-time computational method for tracking and detection of epileptic seizures from raw neurophysiological recordings. Our mechanism is based on the topological analysis of the sliding-window embedding of the time series derived from simultaneously recorded channels. We extract topological biomarkers from the signals via the computation of the persistent homology of time-evolving topological spaces. Remarkably, the proposed biomarkers robustly captures the change in the brain dynamics during the ictal state. We apply our methods in different types of signals including scalp and intracranial electroencephalograms and magnetoencephalograms, in patients during interictal and ictal states, showing high accuracy in a range of clinical situations.
