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 2014
Automatic lung tidal volumes estimation from tracheal sounds.
This paper presents a method to automatically estimate lung tidal volumes from the acoustic signals generated in the respiratory track. The signal is measured with an acoustic based sensor placed in the suprasternal notch. The method does not require any previous knowledge or modelling of the individual respiratory track, and relies on just one calibration parameter. ⋯ The subjects were simultaneously wearing a Wright respirometer which was used as a gold standard for comparison. Agreement between the two methods was assessed with Bland-Altman techniques. The results show the potential the technique has, integrated with a small acoustic sensor, for less-intrusive and even remote and/or continuous monitoring of lung tidal volumes.
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Conf Proc IEEE Eng Med Biol Soc · Jan 2014
Human eyeball model reconstruction and quantitative analysis.
Determining shape of the eyeball is important to diagnose eyeball disease like myopia. In this paper, we present an automatic approach to precisely reconstruct three dimensional geometric shape of eyeball from MR Images. The model development pipeline involved image segmentation, registration, B-Spline surface fitting and subdivision surface fitting, neither of which required manual interaction. ⋯ In addition to the eight metrics commonly used by existing studies, we proposed two novel metrics, Gaussian Curvature Analysis and Sphere Distance Deviation, to quantify the cornea shape and the whole eyeball surface respectively. The experiment results showed that the reconstructed eyeball models accurately represent the complex morphology of the eye. The ten metrics parameterize the eyeball among different subjects, which can potentially be used for eye disease diagnosis.
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Conf Proc IEEE Eng Med Biol Soc · Jan 2014
An adaptive brain-machine interface algorithm for control of burst suppression in medical coma.
Burst suppression is an electroencephalogram (EEG) indicator of profound brain inactivation in which bursts of electrical activity alternate with periods of isoelectricity termed suppression. Specified time-varying levels of burst suppression are targeted in medical coma, a drug-induced brain state used for example to treat uncontrollable seizures. A brain-machine interface (BMI) that observes the EEG could automate the control of drug infusion rate to track a desired target burst suppression trajectory. ⋯ We design an adaptive recursive Bayesian estimator to jointly estimate drug concentrations and system parameters in real time. We construct a controller using the linear-quadratic-regulator strategy that explicitly penalizes large infusion rate variations at steady state and uses the estimates as feedback to generate robust control. Using simulations, we show that the adaptive algorithm achieves precise control of time-varying target levels of burst suppression even when model parameters are initialized randomly, and reduces the infusion rate variation at steady state.
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Conf Proc IEEE Eng Med Biol Soc · Jan 2014
CLINATEC® BCI platform based on the ECoG-recording implant WIMAGINE® and the innovative signal-processing: preclinical results.
The goal of the CLINATEC® Brain Computer Interface (BCI) Project is to improve tetraplegic subjects' quality of life by allowing them to interact with their environment through the control of effectors, such as an exoskeleton. The BCI platform is based on a wireless 64-channel ElectroCorticoGram (ECoG) recording implant WIMAGINE®, designed for long-term clinical application, and a BCI software environment associated to a 4-limb exoskeleton EMY (Enhancing MobilitY). ⋯ Currently, the whole BCI platform was tested in real-time in preclinical experiments carried out in nonhuman primates. In these experiments, the exoskeleton arm was controlled by means of the decoded neuronal activity.
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Conf Proc IEEE Eng Med Biol Soc · Jan 2014
Towards a miniaturized brain-machine-spinal cord interface (BMSI) for restoration of function after spinal cord injury.
Nearly 6 million people in the United States are currently living with paralysis in which 23% of the cases are related to spinal cord injury (SCI). Miniaturized closed-loop neural interfaces have the potential for restoring function and mobility lost to debilitating neural injuries such as SCI by leveraging recent advancements in bioelectronics and a better understanding of the processes that underlie functional and anatomical reorganization in an injured nervous system. ⋯ The paper further presents results from a neurobiological study conducted in both normal and SCI rats to investigate the effect of various ISMS parameters on movement thresholds in the rat hindlimb. Coupled with proper signal-processing algorithms in the future for the transformation between the cortically recorded data and ISMS parameters, such a BMSI has the potential to facilitate functional recovery after an SCI by re-establishing corticospinal communication channels lost due to the injury.