  Nowadays, hospitals are ubiquitous and integral to modern society. Patients
flow in and out of a veritable whirlwind of paperwork, consultations, and
potential inpatient admissions, through an abstracted system that is not
without flaws. One of the biggest flaws in the medical system is perhaps an
unexpected one: the patient alarm system. One longitudinal study reported an
88.8% rate of false alarms, with other studies reporting numbers of similar
magnitudes. These false alarm rates lead to a number of deleterious effects
that manifest in a significantly lower standard of care across clinics.
  This paper discusses a model-based probabilistic inference approach to
identifying variables at a detection level. We design a generative model that
complies with an overview of human physiology and perform approximate Bayesian
inference. One primary goal of this paper is to justify a Bayesian modeling
approach to increasing robustness in a physiological domain.
  We use three data sets provided by Physionet, a research resource for complex
physiological signals, in the form of the Physionet 2014 Challenge set-p1 and
set-p2, as well as the MGH/MF Waveform Database. On the extended data set our
algorithm is on par with the other top six submissions to the Physionet 2014
challenge.
