Journal of clinical monitoring and computing
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J Clin Monit Comput · Aug 1998
Accuracy of volume measurements in mechanically ventilated newborns: a comparative study of commercial devices.
Ventilatory measurements in ventilated newborns are increasingly used to monitor and to optimize mechanical ventilation. The aim of this study was to compare the accuracy of volume measurements by different instruments using standardized laboratory conditions. ⋯ Most of the currently available neonatal spirometry devices allow sufficiently accurate volume measurements in the range of 10-60 ml and at frequencies between 30-60/min provided that an increased FIO2 is taken into account.
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J Clin Monit Comput · Aug 1998
In vivo evaluation of a closed loop monitoring strategy for induced paralysis.
Reliable closed loop infusion systems for regulating paralysis level can be a great convenience to the anesthesiologists in automating their task. This paper describes the in vivo performance evaluation of a self-tuning controller that is designed to accommodate large variations in patient drug sensitivity, drug action delays and environmental interfering noise. ⋯ The system adapted to a large variation in the sample subject drug sensitivity. It remained stable despite large amplitude disturbances and maintained the paralysis at the desired level following the removal of the disturbances.
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In the course of five years the development of an automated anesthesia record keeper has evolved through nearly a dozen stages, each marked by new features and sophistication. Commodity PC hardware and software minimized development costs. ⋯ In addition, we developed an evolutionary strategy that optimized motivation, risk management, and maximized return on investment. Besides providing record keeping services, the system supports educational and research activities and through a flexible plotting paradigm, supports each anesthesiologist's focus on physiological data during and after anesthesia.
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J Clin Monit Comput · Aug 1998
Detection of lung injury with conventional and neural network-based analysis of continuous data.
To test if analysis of pressure and flow waveform patterns with an artificial intelligence neural network could distinguish between normal and injured lungs. ⋯ Normal and fully injured lungs display distinct flow and pressure waveform patterns which are independent of changes in calculated pulmonary mechanics variables. These patterns can be recognized by a neural network. Further research is needed to determine the full potential of automated pattern recognition for lung monitoring.