Likelihood-based artefact detection in continuously-acquired patient vital signs

Robust continuous monitoring of patient vital signs (VS) is limited by artefactual data yielding measurements that are not representative of the patient's physiology. These artefacts are typified by several distinct "archetypes". We present several of these archetypal artefacts for heart rate (HR) monitoring, and propose a light weight, real-time algorithm to remove the majority of these artefacts. Most artefacts are not identifiable by their values in absolute terms, but instead by their values relative to other measurements nearby in time. We model temporally-proximate measurements as independent and identically distributed (i.i.d.) samples from a Gamma distribution. Measurements with low likelihood with respect to the distribution are candidates for artefact removal. This lightweight algorithm is important for real-time deployment on wearable sensors, which are becoming increasingly common in hospital and home care. The clinical applicability of artefact-removal is demonstrated in its ability to enhance patient deterioration detection. A Kalman filter-based patient monitoring algorithm is shown to improve early warning of deterioration when the proposed artefact-removal algorithm is used. We demonstrate this real-time system with patient data from a clinical trial that we have undertaken.