Introducing a 19-s alarm delay and automatically detecting suctioning, repositioning, oral care, and washing could reduce the number of ineffective and ignored alarms from 934 to 274. More reliable alarms could elicit more timely response, reduce workload, reduce noise pollution, and potentially improve patient safety.

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Abstract-This paper shows experimentally that standard wireless networks which measure received signal strength (RSS) can be used to reliably detect human breathing and estimate the breathing rate, an application we call "BreathTaking". We show that although an individual link cannot reliably detect breathing, the collective spectral content of a network of devices reliably indicates the presence and rate of breathing. We present a maximum likelihood estimator (MLE) of breathing rate, amplitude, and phase, which uses the RSS data from many links simultaneously. We show experimental results which demonstrate that reliable detection and frequency estimation is possible with 30 seconds of data, within 0.3 breaths per minute (bpm) RMS error. Use of directional antennas is shown to improve robustness to motion near the network.

This paper provides suggestions for the development of more effective displays in anesthesiology. Graphical displays can increase the anesthesiologist's situation awareness and improve clinical performance. Clinical use of these displays has the potential to significantly improve patient safety.

A theoretical analysis is performed to evaluate the effect of arterial mechanical and blood pressure pulse properties on the accuracy of non-invasive oscillometric maximum amplitude algorithm (MAA) estimates of the mean blood pressure obtained using air-filled occlusive cuffs. Invasively recorded blood pressure pulses, selected for their varied shapes, are scaled to simulate a wide range of blood pulse pressures (diastolic blood pressure minus systolic blood pressure). Each scaled blood pressure pulse is transformed through an exponential model of an artery to create a series of blood volume pulses from which a simulated oscillometric waveform is created and the corresponding MAA estimate of the mean blood pressure and error (mean blood pressure minus MAA estimate) are determined. The MAA estimates are found to depend on the arterial blood pressure. The errors are found to depend on the arterial mechanical properties, blood pressure pulse shape and blood pulse pressure. These results suggest that there is no direct relationship between the mean blood pressure and MAA estimate, and that multiple variables may affect the accuracy of MAA estimates of the mean blood pressure obtained using air-filled occlusive cuffs.