IEEE transactions on bio-medical engineering
-
IEEE Trans Biomed Eng · Mar 2007
ECG signal compression based on Burrows-Wheeler transformation and inversion ranks of linear prediction.
Many transform-based compression techniques, such as Fourier, Walsh, Karhunen-Loeve (KL), wavelet, and discrete cosine transform (DCT), have been investigated and devised for electrocardiogram (ECG) signal compression. However, the recently introduced Burrows-Wheeler Transformation has not been completely investigated. ⋯ We show that when compressing ECG signals, utilization of linear prediction, Burrows-Wheeler Transformation, and inversion ranks yield better compression gain in terms of weighted average bit per sample than recently proposed ECG-specific coders. Not only does our proposed technique yield better compression than ECG-specific compressors, it also has a major advantage: with a small modification, the proposed technique may be used as a universal coder.
-
IEEE Trans Biomed Eng · Mar 2007
A model-based deconvolution approach to solve fiber crossing in diffusion-weighted MR imaging.
A deconvolution approach is presented to solve fiber crossing in diffusion magnetic resonance imaging. In order to provide a direct physical interpretation of the signal generation process, we started from the classical multicompartment model and rewrote this in terms of a convolution process, identifying a significant scalar parameter alpha to characterize the physical system response. ⋯ The in vivo data confirms the efficacy of this method to resolve fiber crossing in real complex brain structures. These results suggest the usefulness of our approach in fiber tracking or connectivity studies.
-
IEEE Trans Biomed Eng · Mar 2007
Encoding of information into neural spike trains in an auditory nerve fiber model with electric stimuli in the presence of a pseudospontaneous activity.
This paper presents an information-theoretic analysis of neural spike trains in an auditory nerve fiber (ANF) model stimulated extracellularly with Gaussian or sinusoidal waveforms in the presence of a pseudospontaneous activity of spike firings. In the computer simulation, stimulus current waveforms were applied repeatedly to a stimulating electrode located 1 mm above the 26th node of Ranvier, in an ANF axon model having 50 nodes of Ranvier, each consisting of stochastic sodium and potassium channels. From spike firing times recorded at the 36th node of Ranvier, a post-stimulus time histogram (PSTH) was generated, and raster plots were depicted for 30 stimulus presentations, in order to investigate the temporal precision and reliability of the spike firing times. ⋯ It was shown in the case of Gaussian electric stimuli that the temporal precision of spike firing times and the reliability of spike firings were found to increase as the standard deviation (SD) of the Gaussian electric stimuli increased. It was also shown in the case of sinusoidal electric stimuli where there was a specific amplitude of sinusoidal waveforms, the information rate being maximized. It was implied that setting the parameters of electric stimuli to the specific values which maximize the information rate might contribute to efficiently encoding information into the spike trains in the presence of a pseudospontaneous activity of spike firings.
-
IEEE Trans Biomed Eng · Mar 2007
A new lumped-parameter model of cerebrospinal hydrodynamics during the cardiac cycle in healthy volunteers.
Our knowledge of cerebrospinal fluid (CSF) hydrodynamics has been considerably improved with the recent introduction of phase-contrast magnetic resonance imaging (phase-contrast MRI), which can provide CSF and blood flow measurements throughout the cardiac cycle. Key temporal and amplitude parameters can be calculated at different sites to elucidate the role played by the various CSF compartments during vascular brain expansion. Most of the models reported in the literature do not take into account CSF oscillation during the cardiac cycle and its kinetic energy impact on the brain. ⋯ These submodels are connected by resistances and compliances. The model developed was used to reproduce certain functional characteristics observed in seven healthy volunteers, such as the distribution (amplitude and phase shift) of arterial, venous, and CSF flows. The results show a good agreement between measured and simulated intracranial CSF and blood flows.
-
IEEE Trans Biomed Eng · Mar 2007
Preprocessing and meta-classification for brain-computer interfaces.
A brain-computer interface (BCI) is a system which allows direct translation of brain states into actions, bypassing the usual muscular pathways. A BCI system works by extracting user brain signals, applying machine learning algorithms to classify the user's brain state, and performing a computer-controlled action. Our goal is to improve brain state classification. ⋯ We apply this procedure to three data sets drawn from BCI Competition 2003 (Blankertz et al., 2004) and BCI Competition III (Blankertz et al., 2006), each of which exhibit very different characteristics. Our final classification results compare favorably with those from past BCI competitions. Additionally, we analyze the relative contributions of individual preprocessing and meta-classification choices and discuss which types of BCI data benefit most from specific algorithms.