Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference
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Conf Proc IEEE Eng Med Biol Soc · Jan 2008
Non-invasive measurement of respiratory rate in children using the photoplethysmogram.
Respiratory rate is recognised as a valuable predictor of the severity of illness in children, but it is not currently feasible to measure this automatically in a triage environment. Autoregressive modelling on data from the pulse oximeter photoplethysmogram has the potential to introduce automated breathing measurement into the realm of paediatric triage. Using autoregressive modelling, it is shown that respiratory rate can be extracted from the paediatric photoplethysmogram with a mean error of 3.4 breaths per minute.
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Conf Proc IEEE Eng Med Biol Soc · Jan 2008
Usefulness of monitoring congestive heart failure by multiple impedance vectors.
We investigated trends in intrathoracic impedance measured between multiple implanted electrodes for monitoring pulmonary edema secondary to congestive heart failure (CHF) in an experimental model. ⋯ All impedance vectors decreased during CHF. Impedance measurement employing left heart sensors correlated well wit- - h LA pressure, and may improve detection of CHF onset compared to sensing by RA or RV leads alone. This approach has important clinical implications for managing heart failure patients in the ambulatory setting.
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Conf Proc IEEE Eng Med Biol Soc · Jan 2008
Target controlled infusion for kids: trials and simulations.
Target controlled infusion (TCI) for Kids is a computer controlled system designed to administer propofol for general anesthesia. A controller establishes infusion rates required to achieve a specified concentration at the drug's effect site (C(e)) by implementing a continuously updated pharmacokinetic-pharmacodymanic model. This manuscript provides an overview of the system's design, preclinical tests, and a clinical pilot study. ⋯ Predicted C(e) values during standard clinical practice, the accuracy of wake-up times predicted by the system, and potential correlations between patient wake-up times, C(e), and state entropy (SE) were assessed. Neither Ce nor SE was a reliable predictor of wake-up time in children, but the small sample size of this study does not fully accommodate the noted variation in children's response to propofol. A C(e) value of 1.9 mug/ml was found to best predict emergence from anesthesia in children.
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Conf Proc IEEE Eng Med Biol Soc · Jan 2008
A new approach to reconstruction of central aortic blood pressure using 'adaptive' transfer function.
This paper presents a new system identification approach to the reconstruction of central aortic blood pressure signal by exploiting a non-invasive peripheral blood pressure measurement. This technique, which is called the 'adaptive' transfer function, is able to reconstruct the aortic blood pressure signal by characterizing the aortic-to-peripheral cardiovascular dynamics solely based on the peripheral measurement. In contrast to the previous related efforts, it does not require any a priori knowledge on the empirical and/or population-based relationship, e.g. the predetermined or generalized transfer function, as well as multiple peripheral measurements. The initial proof-of-principle on the efficacy of the adaptive transfer function is demonstrated by the experimental results from human and animal subjects.
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Conf Proc IEEE Eng Med Biol Soc · Jan 2008
Algorithm for automatic beat detection of cardiovascular pressure signals.
Pressure beat detection is an integral part of most analysis techniques for arterial blood pressure (ABP), intracranial pressure (ICP), and pulse oximetry (SpO(2)) signals. Beat detection has been used to estimate heart rate in the ABP signal, to classify ICP morphologies, and to estimate blood pressure using pulse oximeter waveforms. This paper describes an algorithm that was developed to detect pressure peak beats in ABP, ICP, and SpO(2) signals. When compared to the expert annotation of several signals consisting of over 42,500 pressure beats, the algorithm detected pressure peaks with an average sensitivity of 99.6% +/- 0.27 and an average positive predictivity of 98.6% +/- 1.1.