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 2012
Comparisons of predictors of fluid responsiveness in major surgery.
The majority of studies on fluid responsiveness is focused on volume expansion maneuvers in intensive care unit (ICU), while fewer studies have analyzed the same problem during major surgery. Among them, the results are contrasting. ⋯ Our results showed that pulse pressure variation (PPV) estimated according to the definition, i.e. within single respiratory cycles, and PPV estimated by PiCCO monitor system are coherent and very similar. Moreover, PPV and stroke volume variation (SVV) produced good values of sensitivity and specificity in separating the subjects into responsive and non responsive to maneuvers.
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Conf Proc IEEE Eng Med Biol Soc · Jan 2012
Model based optimization of the cardiopulmonary resuscitation (CPR) procedure.
This paper is concerned with the optimization of the cardiopulmonary resuscitation (CPR) procedure, which plays a critical rule in saving the life of patients suffering from cardiac arrest. In this paper, we define the performance index for optimization using the oxygen delivery. A model developed earlier is used to calculate the oxygen delivery through CPR. ⋯ First, a global optimization is implemented to discover the best values of the free parameters which maximize the oxygen delivery. In addition to this, a sequential optimization scheme is explored which uses a two step optimization in each CPR sequence to maximize the oxygen delivery. Results show that the sequential optimization procedure will enhance the performance of the CPR significantly.
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Conf Proc IEEE Eng Med Biol Soc · Jan 2012
Assessment of ICA algorithms for the analysis of crackles sounds.
Blind source separation by independent component analysis has been applied extensively in the biomedical field for extracting different contributing sources in a signal. Regarding lung sounds analysis to isolate the adventitious sounds from normal breathing sound is relevant. ⋯ Afterwards, Infomax was applied to 25 channels of recorded normal breathing sound where simulated fine and coarse crackles were added including acoustic propagation effects. A robust blind crackle separation could improve previous results in generating an adventitious acoustic thoracic imaging.
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Conf Proc IEEE Eng Med Biol Soc · Jan 2012
Wireless photoplethysmographic device for heart rate variability signal acquisition and analysis.
The photoplethysmographic (PPG) signal has the potential to aid in the acquisition and analysis of heart rate variability (HRV) signal: a non-invasive quantitative marker of the autonomic nervous system that could be used to assess cardiac health and other physiologic conditions. A low-power wireless PPG device was custom-developed to monitor, acquire and analyze the arterial pulse in the finger. The system consisted of an optical sensor to detect arterial pulse as variations in reflected light intensity, signal conditioning circuitry to process the reflected light signal, a microcontroller to control PPG signal acquisition, digitization and wireless transmission, a receiver to collect the transmitted digital data and convert them back to their analog representations. ⋯ Kubios was able to generate a report sheet with the time domain and frequency domain parameters of the acquired data. These features were then compared against those calculated by MATLAB. The preliminary results demonstrate that the prototype wireless device could be used to perform HRV signal acquisition and analysis.
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Conf Proc IEEE Eng Med Biol Soc · Jan 2012
Prediction of extubation failure for neonates with respiratory distress syndrome using the MIMIC-II clinical database.
Extubation failure (EF) is an ongoing problem in the neonatal intensive care unit (NICU). Nearly 25% of neonates fail their first extubation attempt, requiring re-intubations that are associated with risk factors and financial costs. ⋯ From an initial list of 57 candidate features, our machine learning approach narrowed down to six features useful for building an EF prediction model: monocyte cell count, rapid shallow breathing index, fraction of inspired oxygen (FiO(2)), heart rate, PaO(2)/FiO(2) ratio where PaO(2) is the partial pressure of oxygen in arterial blood, and work of breathing index. Algorithm performance had an area under the receiver operating characteristic curve (AUC) of 0.871 and sensitivity of 70.1% at 90% specificity.