自动发现心电图时间序列的动态聚类模式，无需预设聚类数量。
Bayesian Nonparametric Dynamical Clustering of Time Series
- 用分层狄利克雷过程建模未知数量的动态聚类，支持在线更新。
- 在公开心电图数据上实现精准聚类，避免过量分裂，提升稳定性。
- 适合处理长时序、结构多变的生物信号分析，如医疗健康监测。
本文提出一种贝叶斯非参数方法，通过切换线性动态系统（Switching Linear Dynamical System）建模无限数量的时间序列聚类随时间的演化，利用分层狄利克雷过程作为参数先验，并采用高斯过程建模每个聚类内部的幅度波动与时间对齐变化。该方法通过显式建模时间序列模式的演变，在不增加额外聚类的前提下有效防止聚类过度分裂。我们针对离线与在线场景构建了变分下界进行推断，通过优化实现高效学习。多个基于公开数据库的心电图分析案例验证了该方法的通用性与有效性。
原文摘要 · Abstract (English)
We present a method that models the evolution of an unbounded number of time series clusters by switching among an unknown number of regimes with linear dynamics. We develop a Bayesian non-parametric approach using a hierarchical Dirichlet process as a prior on the parameters of a Switching Linear Dynamical System and a Gaussian process prior to model the statistical variations in amplitude and temporal alignment within each cluster. By modeling the evolution of time series patterns, the method avoids unnecessary proliferation of clusters in a principled manner. We perform inference by formulating a variational lower bound for off-line and on-line scenarios, enabling efficient learning through optimization. We illustrate the versatility and effectiveness of the approach through several case studies of electrocardiogram analysis using publicly available databases.
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