Accelerometers improve detection of movement related artifact in bedside ICU data.

Routinely collected real time data from ICU bedside monitors is a valuable source of information which can assist the clinical team caring for a patient. This data almost always contains artifacts caused by clinical staff attending to the patient throughout the day, e.g. blood samples being taken, body washing, administration of IV medication. We present a small (10 patient) study but with accurately annotated clinical event data constructed by observing standard clinical procedures and merging this information with the physiologic data (ECG, ABP, CVP) from bedside monitors. In addition, to assess the feasibility for using accelerometers in the ICU environment, we received ethical approval to place an accelerometer [1] on the chest of all patients and the X, Y & Z accelerometer plane data was merged with the physiological and event annotation data. The non-trivial methods required to capture the data and initial characterisation of results are detailed and discussed. A baseline model cross-correlating ECG and ABP waveform signals over a 10 s window was developed and features derived from this model were used in both GLM and Random Forest (RF) models for predicting events that routinely cause movement related artifact in waveform quality time-series data. RF model performance improved when accelerometer data was added to the model for most movement related artifact types. For the GLM models, only selected models (ANY Type, Turns, Bed Movement and Blood Sample) showed an improvement in model fit with the addition of the accelerometer data (P < 0.01). Implications for observational ICU research and potential "Closed Loop" clinical modelling studies are discussed.