In the field of change-detection changes in the statistical properties of a stochastic process are typically assumed to occur via change-points, which demark instantaneous moments of complete and total change in distribution. In contrast, many real world processes undergo more gradual change in their behavior. With this observation in mind, we introduce a novel change-dynamic model for the online detection of gradual change in which change-points are used within a hierarchical model to indicate moments of gradual change onset or termination. We apply this model to synthetic data and EEG readings drawn during epileptic seizure, finding that our model can afford faster and more accurate identification of gradual change than traditional change-point models allow.
翻译:在随机过程统计特性变化-检测变化领域,通常假设通过变化点发生变化-检测过程的统计特性变化-检测变化-变化,这些变化点标志着分布完全和彻底变化的瞬间瞬间瞬间。相反,许多真实世界过程在行为上发生了更加渐进的变化。考虑到这一观察,我们引入了一种新的变化-动力模型,用于在线检测渐进变化,在等级模型中使用变化点来显示逐渐变化的开始或结束。我们将这一模型应用于在癫痫发作期间提取的合成数据和 EEEG 读数,发现我们的模型能够比传统的改变点模型更快、更准确地识别渐进变化。