A fast and memory-efficient algorithm for learning and retrieval of phenotypic dynamics in multivariate cohort time series

Robust navigation and mining of physiologic time series databases often requires finding similar temporal patterns of physiological responses. Detection of these complex physiological patterns not only enables demarcation of important clinical events but can also elucidate hidden dynamical structures that may be suggestive of disease processes. Some specific examples where this physiological signal search may be useful include real-time detection of cardiac arrhythmias, sleep staging or detection of seizure onset. In all these cases, being able to identify a cohort of patients who exhibit similar physiological dynamics could be useful in prognosis and informing treatment strategies. However, pattern recognition for physiological time series is complicated by changes between operating regimes and measurement artifacts. Here we briefly describe an approach we have developed for distributed identification of dynamical patterns in physiological time series using a switching linear dynamical system (SLDS). We present a fast and memory-efficient algorithm for learning and retrieval of phenotypic dynamics in large clinical time series databases. Through simulation we show that the proposed algorithm is at least an order of magnitude faster that the state of the art, and provide encouraging preliminary results based on real recordings of vital sign time series from the Multiparameter Intelligent Monitoring in Intensive Care (MIMIC-II) database.