Forecasting the state of a system from an observed time series is the subject of research in many domains, such as computational neuroscience. Here, the prediction of epileptic seizures from brain measurements is an unresolved problem. There are neither complete models describing underlying brain dynamics, nor do individual patients exhibit a single seizure onset pattern, which complicates the development of a `one-size-fits-all' solution. Based on a longitudinal patient data set, we address the automated discovery and quantification of statistical features (biomarkers) that can be used to forecast seizures in a patient-specific way. We use existing and novel feature extraction algorithms, in particular the path signature, a recent development in time series analysis. Of particular interest is how this set of complex, nonlinear features performs compared to simpler, linear features on this task. Our inference is based on statistical classification algorithms with in-built subset selection to discern time series with and without an impending seizure while selecting only a small number of relevant features. This study may be seen as a step towards a generalisable pattern recognition pipeline for time series in a broader context.

翻译：从观测时间序列预测系统状态是许多领域的研究主题，例如计算神经科学。其中，基于脑部测量数据预测癫痫发作仍是一个未解决的问题。目前既没有描述大脑底层动力学的完整模型，也没有单个患者表现出单一的发作模式，这阻碍了开发“通用”解决方案。基于纵向患者数据集，我们探索了自动化发现和量化可用于患者特异性癫痫发作预测的统计特征（生物标记物）。我们使用了现有及新型特征提取算法，特别是路径签名——时间序列分析领域的最新进展。我们特别关注这类复杂非线性特征相较于简单线性特征在该任务中的性能表现。我们的推理基于内置子集选择的统计分类算法，通过仅选取少量相关特征来区分即将发作与未发作的时间序列。这项研究可视为向更广泛背景下时间序列通用模式识别流水线迈出的一步。