Despite substantial progress in deep learning approaches to time-series reconstruction, no existing methods are designed to uncover local activities with minute signal strength due to their negligible contribution to the optimization loss. Such local activities however can signify important abnormal events in physiological systems, such as an extra foci triggering an abnormal propagation of electrical waves in the heart. We discuss a novel technique for reconstructing such local activity that, while small in signal strength, is the cause of subsequent global activities that have larger signal strength. Our central innovation is to approach this by explicitly modeling and disentangling how the latent state of a system is influenced by potential hidden internal interventions. In a novel neural formulation of state-space models (SSMs), we first introduce causal-effect modeling of the latent dynamics via a system of interacting neural ODEs that separately describes 1) the continuous-time dynamics of the internal intervention, and 2) its effect on the trajectory of the system's native state. Because the intervention can not be directly observed but have to be disentangled from the observed subsequent effect, we integrate knowledge of the native intervention-free dynamics of a system, and infer the hidden intervention by assuming it to be responsible for differences observed between the actual and hypothetical intervention-free dynamics. We demonstrated a proof-of-concept of the presented framework on reconstructing ectopic foci disrupting the course of normal cardiac electrical propagation from remote observations.
翻译:尽管在对时间序列重建的深层次学习方法方面取得了长足进展,但目前没有设计出任何方法来发现由于对优化损失的贡献微不足道而具有微小信号强度的地方活动。然而,这种地方活动可以表明生理系统中的重要异常事件,例如,在心脏中引发电波异常传播的超微粒效应。我们讨论了重建这种地方活动的新技术,这种地方活动虽然信号强度较小,但却是随后全球活动具有更大信号力量的原因。我们的核心创新是通过明确模拟和分解一个系统的潜在潜伏状态如何受到潜在隐藏的内部干预影响来解决这一问题。在对州空间模型(SSMS)进行新颖的神经构思时,我们首先通过一个互动神经代码系统引入潜在动态的因果关系模型,分别描述1)内部干预的连续时间动态,2)对系统本地状态的轨迹产生影响。由于干预不能直接观察,但不得不与观察到的随后效果分解,我们综合了对系统本土干预动态的潜在影响的知识,并且通过假设我们对所观察到的正常的动态的正常状态进行隐蔽的观察,从而推导出一种对我们所观察到的正常的正常的动态结构结构结构结构的变。