Probabilistic Model-Based Approach for Heart Beat Detection

TL;DR

This paper introduces a probabilistic Bayesian inference method for heart beat detection aimed at reducing false alarms in hospital monitoring systems, leveraging physiological models and real-world datasets.

Contribution

It proposes a novel generative model and Bayesian inference approach for physiological signal analysis, demonstrating competitive performance on standard datasets.

Findings

Achieved performance comparable to top challenge submissions

Validated model robustness across multiple datasets

Showed potential to reduce false alarms in clinical settings

Abstract

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.