IEEE transactions on bio-medical engineering
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IEEE Trans Biomed Eng · Nov 2006
Adaptive change detection in heart rate trend monitoring in anesthetized children.
The proposed algorithm is designed to detect changes in the heart rate trend signal which fits the dynamic linear model description. Based on this model, the interpatient and intraoperative variations are handled by estimating the noise covariances via an adaptive Kalman filter. An exponentially weighted moving average predictor switches between two different forgetting coefficients to allow the historical data to have a varying influence in prediction. ⋯ The algorithm was tested on a substantial volume of real clinical data. Comparison of the proposed algorithm with Trigg's approach revealed that the algorithm performs more favorably with a shorter delay. The receiver operating characteristic curve analysis indicates that the algorithm outperformed the change detection by clinicians in real time.
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IEEE Trans Biomed Eng · Nov 2006
Boundary enhancement and speckle reduction for ultrasound images via salient structure extraction.
In this paper, we present an approach for medical ultrasound (US) image enhancement. It is based on a novel perceptual saliency measure which favors smooth, long curves with constant curvature. The perceptual salient boundaries of tissues in US images are enhanced by computing the saliency of directional vectors in the image space, via a local searching algorithm. ⋯ To restrain speckle noise during the enhancement process, an adaptive speckle suspension term is also combined into the proposed saliency measure. The results obtained on both simulated images and medical US data reveal superior performance of the novel approach over a number of commonly used speckle filters. Applications of US image segmentation show that although the proposed algorithm cannot remove the speckle noise completely and may discard weak anatomical structures in some case, it still provides a considerable gain to US image processing for computer-aided diagnosis.
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IEEE Trans Biomed Eng · Oct 2006
Simulation of intramuscular EMG signals detected using implantable myoelectric sensors (IMES).
The purpose of this study was to test the feasibility of recording independent electromyographic (EMG) signals from the forearm using implantable myoelectric sensors (IMES), for myoelectric prosthetic control. Action potentials were simulated using two different volume conductor models: a finite-element (FE) model that was used to explore the influence of the electrical properties of the surrounding inhomogeneous tissues and an analytical infinite volume conductor model that was used to estimate the approximate detection volume of the implanted sensors. ⋯ Changing the orientation of the electrode with respect to the fiber direction altered the shape of the electrode detection volume and reduced the electrode selectivity. The estimated detection radius of the IMES electrode, assuming a cylindrical muscle cross section, was 4.8, 6.2, and 7.5 mm for electrode orientations of 0 degree, 22.5 degrees, and 45 degrees with respect to the muscle fiber direction.
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IEEE Trans Biomed Eng · Oct 2006
Clinical TrialForecasting the unresponsiveness to verbal command on the basis of EEG frequency progression during anesthetic induction with propofol.
The objective of this study is to model the association between the electroencephalogram (EEG) spectral features and the novel r scale representing the sedative effects of the propofol anesthetic drug. On the basis of the r scale, the unresponsiveness to the verbal command (LVC) is forecasted. EEG recordings are taken from a 16-patient study population undergoing propofol anesthetic induction. ⋯ The results suggest an acceptable correlation between the r scale and the EEG spectrum in the studied range. Moreover, the r values of an individual can be predicted using a population model. The suggested framework enables forecasting the LVC, which may open new possibilities for steering the drug administration.
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IEEE Trans Biomed Eng · Oct 2006
Estimation of motor unit conduction velocity from surface EMG recordings by signal-based selection of the spatial filters.
Muscle fiber conduction velocity (CV) can be estimated by the application of a pair of spatial filters to surface electromagnetic (EMG) signals and compensation of the spatial filter transfer function with equivalent temporal filters. This method integrates the selection of the spatial filters for signal detection to the estimation of CV. Using this approach, in this paper, we propose a novel technique for signal-based selection of the spatial filter pair that minimizes the effect of nonpropagating signal components (end-of-fiber effects) on CV estimates (optimal filters). ⋯ In the simulations, the proposed approach provided CV estimates with lower bias due to nonpropagating signal components than previously proposed methods based on the entire signal waveform. In the experimental signals, the technique separated propagating and nonpropagating signal components with an average reconstruction error of 2.9 +/- 0.9% of the signal energy. The technique may find application in single motor unit studies for decreasing the variability and bias of CV estimates due to the presence and different weights of the nonpropagating components.