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 2011
Developing quantitative physiological phenotypes of sleep apnea for epidemiological studies.
Existing physiological databases have not been sufficiently detailed to provide relevant and important information for characterizing the pathophysiology of obstructive sleep apnea. Critical collapsing pressure (P(CRIT)) is a standard method for determining upper airway patency during sleep, however is labor intensive and prohibits large-scale studies. Based on previously published data indicating R(US) does not significantly vary between groups, our aim was to develop an approach to estimate the P(CRIT) from airflow at atmospheric pressure (V(atm)). ⋯ The variance of upstream resistance approaches a constant value in groups with approximately 40 subjects. Utilizing a fixed up-stream resistance to estimate P(CRIT) from the airflow at atmospheric pressure agrees with the measured values. These data suggest that measurements of quantitative airflow during standard polysomnography can be used to determine upper airway properties in large cohorts.
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Conf Proc IEEE Eng Med Biol Soc · Jan 2011
Analysis of the respiratory pattern variability of patients in weaning process using autoregressive modeling techniques.
One of the most challenging problems in intensive care is the process of discontinuing mechanical ventilation, called weaning process. An unnecessary delay in the discontinuation process and an early weaning trial are undesirable. This paper proposes to analysis the respiratory pattern variability of these patients using autoregressive modeling techniques: autoregressive models (AR), autoregressive moving average models (ARMA), and autoregressive models with exogenous input (ARX). ⋯ The respiratory pattern was characterized by their time series. The results show that significant differences were obtained with parameters as model order and first coefficient of AR model, and final prediction error by ARMA model. An accuracy of 86% (84% sensitivity and 86% specificity) has been obtained when using order model and first coefficient of AR model, and mean of breathing duration.
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Conf Proc IEEE Eng Med Biol Soc · Jan 2011
EMG contributes to improve Cerebral State Index modeling in dogs anesthesia.
Cerebral State Index (CSI) is a measure of depth of anesthesia (DoA) developed for humans, which is traditionally modeled with the Hill equation and the propofol effect-site concentration (Ce). The CSI has been studied in dogs and showed several limitations related to the interpretation of EEG data. Nevertheless, the CSI has a lot of potential for DoA monitoring in dogs, it just needs to be adjusted for this species. ⋯ In fact, the EMG introduction in CSI model significantly decreased the modeling error: 11.8 [8.6; 15.2] (fuzzy logic) versus 20.9 [16.4; 29.0] (Hill). This work shows that CSI modeling in dogs can be improved using the current human anesthesia set-up, once the EMG signal is acquired simultaneously with the CSI index. However, it does not invalidate the search of new DoA indices more adjusted to use in dog's anesthesia.
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Conf Proc IEEE Eng Med Biol Soc · Jan 2011
Bayesian analysis of trinomial data in behavioral experiments and its application to human studies of general anesthesia.
Accurate quantification of loss of response to external stimuli is essential for understanding the mechanisms of loss of consciousness under general anesthesia. We present a new approach for quantifying three possible outcomes that are encountered in behavioral experiments during general anesthesia: correct responses, incorrect responses and no response. ⋯ We show applications of this approach to an example of responses to auditory stimuli at varying levels of propofol anesthesia ranging from light sedation to deep anesthesia in human subjects. The posterior probability densities of model parameters and the response probability are computed within a Bayesian framework using Markov Chain Monte Carlo methods.
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Conf Proc IEEE Eng Med Biol Soc · Jan 2011
Closing the loop for Deep Brain Stimulation implants enables personalized healthcare for Parkinson's disease patients.
DEEP brain stimulation implants have improved life quality for more than 70,000 patients world-wide with diseases like Parkinson's, essential tremor, or obsessive-compulsive disorder where pharmaceutical therapies alone could not offer sufficient relief. Still, optimization and monitoring relies heavily on regular clinical visits, putting a burden on patient's comfort and clinicians. Permanent monitoring and combination with other patient health signals could ultimately lead to a personalized closed-loop therapy with remote quality monitoring. This requires technological improvements on the DBS implants such as integration of recording capabilities for brain activity monitoring, active low-power electronics, rechargeable battery technology, and body sensor networks for integration with e.g. gait, speech, and other vital information sensors on the patient's body and a link to a telemedicine platform using mobile technologies.