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PubMed2002

Mutual information-based rigid and nonrigid registration of ultrasound volumes.

We investigated the registration of ultrasound volumes based on the mutual information measure, a technique originally applied to multimodality registration of brain images. A prerequisite for successful registration is a smooth, quasi-convex mutual information surface with an unambiguous maximum. We discuss the necessary preprocessing to create such a surface for ultrasound volumes. Abdominal and thoracic organs imaged with ultrasound typically move relative to the exterior of the body and are deformable. Consequently, four specific instances of image registration involving progressively generalized transformations were studied: rigid-body, rigid-body + uniform scaling, rigid-body + nonuniform scaling, and affine. Registration was applied to clinically acquired volumetric images. The accuracy was comparable with the voxel dimension for all transformation modes, although it degraded as the transformation grew more complex. Likewise, the capture range became narrower with the complexity of transformation. As the use of real-time three-dimensional ultrasound becomes more prevalent, the method we present should work well for a variety of applications examining serial anatomic and physiologic changes. Developers of these clinical applications would match the deformation model of their problem to one of the four transformation models presented here.

Algorithms↗
PubMed2002

Computation of the mid-sagittal plane in 3-D brain images.

We present a new method to automatically compute, reorient, and recenter the mid-sagittal plane in anatomical and functional three-dimensional (3-D) brain images. This iterative approach is composed of two steps. At first, given an initial guess of the mid-sagittal plane (generally, the central plane of the image grid), the computation of local similarity measures between the two sides of the head allows to identify homologous anatomical structures or functional areas, by way of a block matching procedure. The output is a set of point-to-point correspondences: the centers of homologous blocks. Subsequently, we define the mid-sagittal plane as the one best superposing the points on one side and their counterparts on the other side by reflective symmetry. Practically, the computation of the parameters characterizing the plane is performed by a least trimmed squares estimation. Then, the estimated plane is aligned with the center of the image grid, and the whole process is iterated until convergence. The robust estimation technique we use allows normal or abnormal asymmetrical structures or areas to be treated as outliers, and the plane to be mainly computed from the underlying gross symmetry of the brain. The algorithm is fast and accurate, even for strongly tilted heads, and even in presence of high acquisition noise and bias field, as shown on a large set of synthetic data. The algorithm has also been visually evaluated on a large set of real magnetic resonance (MR) images. We present a few results on isotropic as well as anisotropic anatomical (MR and computed tomography) and functional (single photon emission computed tomography and positron emission tomography) real images, for normal and pathological subjects.

Algorithms↗
PubMedDate not supplied

Estimation and application of EMG amplitude during dynamic contractions.

The sections above have described an EMG amplitude estimator and an initial application of this estimator to the EMG-torque problem. The amplitude estimator consists of six stages. In the first stage, motion artifact and power-line interference are attenuated. Motion artifact is typically removed with a highpass filter. Elimination of power-line noise is more difficult. Commercial systems tend to use notch filters, accepting the concomitant loss of "true" signal power in exchange for simplicity and robustness. Adaptive methods may be preferable, however, to preserve more "true" signal power. In stage two, the signal is whitened. One fixed whitening technique and two adaptive whitening methods were described. For low-amplitude levels, the adaptive whitening technique that includes adaptive noise cancellation may be necessary. In stage three, multiple EMG channels (all overlying the same muscle) are combined. For most applications, simple gain normalization is all that is required. Stage four rectifies the signal and then applies the power law required to demodulate the signal. In stage six, the inverse of the power law is applied to relinearize the signal. Direct comparison of MAV (first power) to RMS (second power) processing demonstrates little difference between the two. Therefore, unless there is reason to believe that the EMG density departs strongly from that found in the existing studies, RMS and MAV processing are essentially identical. In stage five, the demodulated samples are averaged across all channels and then smoothed (time averaged) to reduce the variance of the amplitude estimate, but at the expense of increasing the bias. For best performance, the window length that best trades off variance and bias error is selected. The advanced EMG processing was next applied to dynamic EMG-torque estimation about the elbow joint. Results showed that improved EMG amplitude estimates led to improved EMG-torque estimates. An initial comparison of different system-identification techniques and model orders was reported. It is expected that these advanced processing and identification algorithms will also improve performance in other EMG applications, including myoelectrically controlled prostheses, biofeedback, and ergonomic assessment.

Algorithms↗
PubMed2002

Spatiotemporal EEG/MEG source analysis based on a parametric noise covariance model.

A method is described to incorporate the spatiotemporal noise covariance matrix into a spatiotemporal source analysis. The essential feature is that the estimation problem is split into two parts. First, a model is fitted to the observed noise covariance matrix. This model is a Kronecker product of a spatial and a temporal matrix. The spatial matrix models the spatial covariances by a function dependent on sensor distance. The temporal matrix models the temporal covariances as lag dependent. In the second part, sources are estimated given this noise model, which can be done very efficiently due to the Kronecker formulation. An application to real electroencephalogram (EEG) data shows that the noise model fits the data very well. Simulation results show that the resulting source estimates are more precise than those obtained from a standard analysis neglecting the noise covariance. In addition, the estimated standard errors of the source parameter estimates are far more precise than those obtained from a standard analysis. Finally, the source parameter standard errors are used to investigate the effects of temporal sampling. It is shown that increasing the sampling by a factor x, decreases the standard errors of all source parameters with the square root of x.

Computer Simulation↗
PubMed2002

Nonlinear nonstationary Wiener model of infant EEG seizures.

This paper presents the estimation of a nonstationary nonlinear model of seizures in infants based on parallel Wiener structures. The model comprises two parts and is partly derived from the Roessgen et al. seizure model. The first part consists of a nonlinear Wiener model of the pure background activity, and the second part in a nonlinear Wiener model of the pure seizure activity with a time-varying deterministic input signal. The two parts are then combined in a parallel structure. The Wiener model consists of an autoregressive moving average filter followed by a nonlinear shaping function to take into account the non-Gaussian statistical behavior of the data. Model estimation was performed on 64 infants of whom four showed signs of clinical and electrical seizures. Model validation is performed using time-frequency-based entropy distance and shows an averaged improvement of 50% in modeling performance compared with the Roessgen model.

Electroencephalography↗
PubMed2002

Stimulus artifact cancellation in the serosal recordings of gastric myoelectric activity using wavelet transform.

Previous studies have shown that electrical stimulation of the stomach (i.e., gastric pacing) with appropriate parameters is a promising method for treatment of gastroparetic patients. The recording of gastric myoelectric activity (GMA) by serosal electrodes is often used to evaluate the effect of stimulation. However, the major problem with the measurement of GMA during gastric pacing is the stimulus artifacts which are often superimposed on the serosal recording and make analysis difficult. The frequency-domain adaptive filter has been used to reduce the stimulus artifacts but only with limited success. This paper describes a wavelet transform-based method for the reduction of stimulus artifacts in the serosal recordings of GMA. The key of this method lies in the use of the fuzzy set theory to select the stimulus artifact-related modulus maxima in the wavelet domain. Both quantitative and qualitative measures show that significant stimulus artifact cancellation was achieved through a series of computer simulations. Results from both single- and multichannel serosally recorded myoelectric signals during gastric pacing are presented to demonstrate the efficiency of the proposed method for the cancellation of stimulus artifacts.

Algorithms↗
PubMed2002

Noninvasive detection of fibrillation potentials in skeletal muscle.

The presence of spontaneous muscle activity was determined by analysis of the power spectra of computer-model-generated sequences of spontaneous activity and additive noise. The modeling results identified the frequency band of 100-300 Hz as the band of peak signal-to-noise ratio for the detection of fibrillation potentials. Animal experiments were conducted in which the left sciatic nerves of three rats were transected. Measurements were taken 14 days following surgery with Ag/AgCl gel electrodes on the skin surface. Data was recorded from the gastrocnemius muscle on both the normal and denervated side for all three rats. The normal data and the denervated data yielded no discernible difference in the time-domain. Spectral analysis, however, demonstrated a clear and quantifiable difference between denervated and normal muscle signals. The average difference between the denervated and normal power spectral densities for the frequency band from 100 Hz to 300 Hz was 3.43, 1.90, and 3.02 dB for the three rats. The additional energy observed in the signals recorded from denervated muscles suggests that the single fiber spontaneous muscle activity that occurs in denervated muscle can be noninvasively detected. The potential diagnostic utility of noninvasive fibrillation potential detection is discussed and suggestions for future experiments are made.

Action Potentials↗
PubMed2002

Robust weighted averaging.

Signal averaging is often used to extract a useful signal embedded in noise. This method is especially useful for biomedical signals, where the spectra of the signal and noise significantly overlap. In this case, traditional filtering techniques introduce unacceptable signal distortion. In averaging methods, constancy of the noise power is usually assumed, but in reality noise features a variable power. In this case, it is more appropriate to use a weighted averaging. The main problem in this method is the estimation of the noise power in order to obtain the weight values. Additionally, biomedical signals often contain outliers. This requires robust averaging methods. This paper shows that signal averaging can be formulated as a problem of minimization of a criterion function. Based on this formulation new weighted averaging methods are introduced, including weighted averaging based on criterion function minimization (WACFM) and robust epsilon-insensitive WACFM. Performances of these new methods are experimentally compared with the traditional averaging and other weighted averaging methods using electrocardiographic signal with the muscle noise, impulsive noise, and time-misalignment of cycles. Finally, an application to the late potentials extraction is shown.

Algorithms↗
PubMed2002

Time-frequency spectral estimation of multichannel EEG using the Auto-SLEX method.

In this paper, we apply a new time-frequency spectral estimation method for multichannel data to epileptiform electroencephalography (EEG). The method is based on the smooth localized complex exponentials (SLEX) functions which are time-frequency localized versions of the Fourier functions and, hence, are ideal for analyzing nonstationary signals whose spectral properties evolve over time. The SLEX functions are simultaneously orthogonal and localized in time and frequency because they are obtained by applying a projection operator rather than a window or taper. In this paper, we present the Auto-SLEX method which is a statistical method that 1) computes the periodogram using the SLEX transform, 2) automatically segments the signal into approximately stationary segments using an objective criterion that is based on log energy, and 3) automatically selects the optimal bandwidth of the spectral smoothing window. The method is applied to the intracranial EEG from a patient with temporal lobe epilepsy. This analysis reveals a reduction in average duration of stationarity in preseizure epochs of data compared to baseline. These changes begin up to hours prior to electrical seizure onset in this patient.

Algorithms↗
PubMed2002

Matched-filter template generation via spatial filtering: application to fetal biomagnetic recordings.

We have developed a two-step procedure for signal processing of fetal biomagnetic recordings that removes cardiac interference and noise. First, a modified matched filter (MF) is applied to remove maternal cardiac interference; then, a simple signal space projection (SSP) is applied to remove noise. The key difference between our MF and a conventional one is that the interference template and the template scaling are derived from a signal that has been spatially filtered to isolate the interference, rather than from the raw signal. Unlike conventional MFs, ours is able to separate maternal and fetal cardiac complexes, even when they have similar morphology and overlap strongly. When followed by a SSP that preserves only the signal subspace, the noise is reduced to a low level.

Electrocardiography↗
PubMed2002

Time-varying properties of renal autoregulatory mechanisms.

In order to assess the possible time-varying properties of renal autoregulation, time-frequency and time-scaling methods were applied to renal blood flow under broad-band forced arterial blood pressure fluctuations and single-nephron renal blood flow with spontaneous oscillations obtained from normotensive (Sprague-Dawley, Wistar, and Long-Evans) rats, and spontaneously hypertensive rats. Time-frequency analyses of normotensive and hypertensive blood flow data obtained from either the whole kidney or the single-nephron show that indeed both the myogenic and tubuloglomerular feedback (TGF) mechanisms have time-varying characteristics. Furthermore, we utilized the Renyi entropy to measure the complexity of blood-flow dynamics in the time-frequency plane in an effort to discern differences between normotensive and hypertensive recordings. We found a clear difference in Renyi entropy between normotensive and hypertensive blood flow recordings at the whole kidney level for both forced (p < 0.037) and spontaneous arterial pressure fluctuations (p < 0.033), and at the single-nephron level (p < 0.008). Especially at the single-nephron level, the mean Renyi entropy is significantly larger for hypertensive than normotensive rats, suggesting more complex dynamics in the hypertensive condition. To further evaluate whether or not the separation of dynamics between normotensive and hypertensive rats is found in the prescribed frequency ranges of the myogenic and TGF mechanisms, we employed multiresolution wavelet transform. Our analysis revealed that exclusively over scale ranges corresponding to the frequency intervals of the myogenic and TGF mechanisms, the widths of the blood flow wavelet coefficients fall into disjoint sets for normotensive and hypertensive rats. The separation of the scales at the myogenic and TGF frequency ranges is distinct and obtained with 100% accuracy. However, this observation remains valid only for the whole kidney blood pressure/flow data. The results suggest that understanding of the time-varying properties of the two mechanisms is required for a complete description of renal autoregulation.

Algorithms↗