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Early detection of deterioration in hospital patients followed by intervention and stabilization can prevent adverse events such as a cardiac arrest, unscheduled admission to ICU, or death. Patients at step-down units of hospitals tend to have their vital signs checked by nursing staff at 4-hourly intervals. If an abnormality develops in the period between nurse observations, it is likely to lead to an adverse event (which may have been preventable). Visensia is a real-time, continuous vital sign acquisition system, using data fusion in order to predict patient deterioration. Validation trials have shown that the system successfully provides early warning of adverse events, such as cardiac arrests. We tested the system on lower acuity, ambulatory patients in a hospital ward with the vital signs being collected using telemetry. In order to optimize processing, we have developed an algorithm for deriving the respiration rate of the patient from the ECG signal.

Original publication




Conference paper

Publication Date





4650 - 4653


Humans, Electrocardiography, Ambulatory, Oximetry, Monitoring, Ambulatory, Telemetry, Reproducibility of Results, Algorithms, Signal Processing, Computer-Assisted, Hospitals, Respiratory Rate