Model identification for physiological systems is complicated by changes between operating regimes and mea- surement artifacts. We present a solution to these problems by assuming that a cohort of physiological time series is gener- ated by switching among a finite collection of physiologically-constrained dynamical models and artifactual segments. We model the resulting time series using the switching linear dynamical systems (SLDS) framework, and present a novel learning algorithm for the class of SLDS, with the objective of identifying time series dynamics that are predictive of physiological regimes or outcomes of interest. We present exploratory results based on a simulation study and a physiological classification example of decoding postural changes from heart rate and blood pressure. We demonstrate a significant improvement in classification over methods based on feature learning via expectation maximization. The proposed learning algorithm is general, and can be extended to other applications involving state-space formulations.
@conference{nemati2013learning,
year = {2013},
author = {Nemati, Shamim and Lehman, Li-Wei and Adams, Ryan P.},
title = {Learning Outcome-Discriminative Dynamics in Multivariate Physiological Cohort Time Series},
booktitle = {Proceedings of the 35th International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)},
keywords = {time series, biomedical engineering, EMBC}
}