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S Van Huffel

Publications and source records attributed to S Van Huffel.

At least 37 records · Page 2Linked to original sources
PubMed2004

Detection of fast neuronal signals in the motor cortex from functional near infrared spectroscopy measurements using independent component analysis.

Fast changes, in the range of milliseconds, in the optical properties of cerebral tissue are associated with brain activity and can be detected using non-invasive near-infrared spectroscopy (NIRS). These changes are assumed to be caused by changes in the light scattering properties of the neuronal tissue. The aim of this study was to develop highly sensitive data analysis algorithms to detect this fast signal, which is small compared with other physiological signals. A frequency-domain tissue oximeter, whose laser diodes were intensity modulated at 110 MHz, was used. The amplitude, mean intensity and phase of the modulated optical signal were measured at a sample rate of 96 Hz. The probe, consisting of four crossed source detector pairs was placed above the motor cortex, contralateral to the hand performing a tapping exercise consisting of alternating rest and tapping periods of 20 s each. An adaptive filter was used to remove the arterial pulsatility from the optical signals. Independent component analysis allowed further separation of a signal component containing the fast signal. In nine out of 14 subjects, a significant fast neuronal signal related to the finger tapping was found in the intensity signals. In the phase signals, indications of the fast signal were found in only two subjects.

Adult↗
PubMed2004

Brain tumor classification based on long echo proton MRS signals.

There has been a growing research interest in brain tumor classification based on proton magnetic resonance spectroscopy (1H MRS) signals. Four research centers within the EU funded INTERPRET project have acquired a significant number of long echo 1H MRS signals for brain tumor classification. In this paper, we present an objective comparison of several classification techniques applied to the discrimination of four types of brain tumors: meningiomas, glioblastomas, astrocytomas grade II and metastases. Linear and non-linear classifiers are compared: linear discriminant analysis (LDA), support vector machines (SVM) and least squares SVM (LS-SVM) with a linear kernel as linear techniques and LS-SVM with a radial basis function (RBF) kernel as a non-linear technique. Kernel-based methods can perform well in processing high dimensional data. This motivates the inclusion of SVM and LS-SVM in this study. The analysis includes optimal input variable selection, (hyper-) parameter estimation, followed by performance evaluation. The classification performance is evaluated over 200 stratified random samplings of the dataset into training and test sets. Receiver operating characteristic (ROC) curve analysis measures the performance of binary classification, while for multiclass classification, we consider the accuracy as performance measure. Based on the complete magnitude spectra, automated binary classifiers are able to reach an area under the ROC curve (AUC) of more than 0.9 except for the hard case glioblastomas versus metastases. Although, based on the available long echo 1H MRS data, we did not find any statistically significant difference between the performances of LDA and the kernel-based methods, the latter have the strength that no dimensionality reduction is required to obtain such a high performance.

Artificial Intelligence↗
PubMed2004

Subspace-based MRS data quantitation of multiplets using prior knowledge.

Accurate quantitation of Magnetic Resonance Spectroscopy (MRS) signals is an essential step before converting the estimated signal parameters, such as frequencies, damping factors, and amplitudes, into biochemical quantities (concentration, pH). Several subspace-based parameter estimators have been developed for this task, which are efficient and accurate time-domain algorithms. However, they suffer from a serious drawback: they allow only a limited inclusion of prior knowledge which is important for accuracy and resolution. In this paper, a new method is presented: KNOB-SVD and its improved variant KNOB-TLS. KNOB-SVD is a recently proposed method, based on the Singular Value Decomposition (SVD), which allows the use of more prior knowledge about the signal parameters than previously published subspace-based methods. We compare its performance in terms of robustness and accuracy with the performance of three commonly used methods for signal parameter estimation: HTLS, a subspace-based method which does not allow any inclusion of prior knowledge, except for the model order; HTLSPK(Delta fd(eq)), a subspace-based method obtained by incorporating in HTLS the prior information that the frequency differences between doublet components are known and the damping factors are equal; and AMARES, an interactive maximum likelihood method that allows the inclusion of a variety of prior knowledge. Extensive simulation and in vivo studies, using (31)P as well as proton MRS signals, show that the new method outperforms HTLS and HTLSPK(Delta fd(eq)) in robustness, accuracy, and resolution, and that it provides parameter estimates comparable to the AMARES ones.

Journal Article↗
PubMed2004

Classification of brain tumours using short echo time 1H MR spectra.

The purpose was to objectively compare the application of several techniques and the use of several input features for brain tumour classification using Magnetic Resonance Spectroscopy (MRS). Short echo time 1H MRS signals from patients with glioblastomas (n = 87), meningiomas (n = 57), metastases (n = 39), and astrocytomas grade II (n = 22) were provided by six centres in the European Union funded INTERPRET project. Linear discriminant analysis, least squares support vector machines (LS-SVM) with a linear kernel and LS-SVM with radial basis function kernel were applied and evaluated over 100 stratified random splittings of the dataset into training and test sets. The area under the receiver operating characteristic curve (AUC) was used to measure the performance of binary classifiers, while the percentage of correct classifications was used to evaluate the multiclass classifiers. The influence of several factors on the classification performance has been tested: L2- vs. water normalization, magnitude vs. real spectra and baseline correction. The effect of input feature reduction was also investigated by using only the selected frequency regions containing the most discriminatory information, and peak integrated values. Using L2-normalized complete spectra the automated binary classifiers reached a mean test AUC of more than 0.95, except for glioblastomas vs. metastases. Similar results were obtained for all classification techniques and input features except for water normalized spectra, where classification performance was lower. This indicates that data acquisition and processing can be simplified for classification purposes, excluding the need for separate water signal acquisition, baseline correction or phasing.

Brain Chemistry↗
PubMed2004

Continuous measurement of cerebral blood volume and oxygenation during rewarming of neonates.

AIM: To investigate the effect of rewarming in preterm infants presenting with hypothermia at admission. METHODS: The tissue oxygenation index (TOI), changes in cerebral blood volume (DeltaCBV) and changes in intravascular oxygenation (DeltaHbD) were measured in eight preterm infants, presenting with a temperature less than 35 degrees C at admission. RESULTS: A significant increase in HbD and TOI was seen in four patients (group A), while a significant increase in CBV and a decrease in HbD was seen in four other patients (group B). Retrospective analysis showed that group A had important signs of peripartal asphyxia. CONCLUSION: While infants with peripartal asphyxia showed an important increase in oxygenation during rewarming, no significant changes were seen in the non-asphyxiated infants.

Asphyxia Neonatorum↗
PubMed2003

Quantification of the glycogen 13C-1 NMR signal during glycogen synthesis in perfused rat liver.

We studied glycogen synthesis from glucose in perfused livers of fed (n = 4) and 24 h starved (n = 7) rats. Glycogenolysis was inhibited by BAY R3401 (150 microM) and proglycosyn (100 microM). After 60 min, we replaced 99% (13)C-1 glucose by natural abundance glucose. This pulse-chase design allowed us to recognize residual ongoing futile glycogen turnover from the release of initially deposited (13)C-label, into the (13)C-free chase medium. Net residual turnover was less than 2 +/- 0.7% and 0.6 +/- 0.2% of 1-(13)C glycogen deposition rates of 0.31 +/- 0.04 and 0.99 +/- 0.04 micromol glucose g(-1) min(-1), in starved and fed livers, respectively. The 1-(13)C glycogen signal was monitored throughout the experiment with proton-decoupled (13)C NMR spectroscopy and analyzed in the time domain using AMARES. We noticed progressive line-broadening in any single experiment in the chase phase. One or a sum of two to three overlapping Lorentzians, with different exponential damping factors, were fitted to the signal. When the S/N was better than 40, the fit always delivered a small and a broad component. In the chase phase, the fit with a single Lorentzian resulted in a decline of glycogen signal by about 15 +/- 4 and 12 +/- 2% in starved and fed rats, respectively. This apparent decline in 1-(13)C glycogen signal could not be accounted for by the appearance of equivalent amounts of (13)C-labeled metabolites in the perfusate. The fit with a sum of two Lorentzians resulted in a decline of glycogen signal intensity of 7 +/- 5 and 5 +/- 3% in starved and fed rats, respectively, which reduced the apparent turnover to 8 +/- 9% and 6 +/- 4%, respectively. Quantification of the growing (13)C-1 glycogen signal requires a model function that accommodates changes in line shape throughout the period under study.

Algorithms↗
PubMed2003

Preoperative prediction of malignancy of ovarian tumors using least squares support vector machines.

In this work, we develop and evaluate several least squares support vector machine (LS-SVM) classifiers within the Bayesian evidence framework, in order to preoperatively predict malignancy of ovarian tumors. The analysis includes exploratory data analysis, optimal input variable selection, parameter estimation, and performance evaluation via receiver operating characteristic (ROC) curve analysis. LS-SVM models with linear and radial basis function (RBF) kernels, and logistic regression models have been built on 265 training data, and tested on 160 newly collected patient data. The LS-SVM model with nonlinear RBF kernel achieves the best performance, on the test set with the area under the ROC curve (AUC), sensitivity and specificity equal to 0.92, 81.5% and 84.0%, respectively. The best averaged performance over 30 runs of randomized cross-validation is also obtained by an LS-SVM RBF model, with AUC, sensitivity and specificity equal to 0.94, 90.0% and 80.6%, respectively. These results show that the LS-SVM models have the potential to obtain a reliable preoperative distinction between benign and malignant ovarian tumors, and to assist the clinicians for making a correct diagnosis.

Adnexal Diseases↗
PubMed2002

Improved Lanczos algorithms for blackbox MRS data quantitation.

Magnetic resonance spectroscopy (MRS) has been shown to be a potentially important medical diagnostic tool. The success of MRS depends on the quantitative data analysis, i.e., the interpretation of the signal in terms of relevant physical parameters, such as frequencies, decay constants, and amplitudes. A variety of time-domain algorithms to extract parameters have been developed. On the one hand, there are so-called blackbox methods. Minimal user interaction and limited incorporation of prior knowledge are inherent to this type of method. On the other hand, interactive methods exist that are iterative, require user involvement, and allow inclusion of prior knowledge. We focus on blackbox methods. The computationally most intensive part of these blackbox methods is the computation of the singular value decomposition (SVD) of a Hankel matrix. Our goal is to reduce the needed computational time without affecting the accuracy of the parameters of interest. To this end, algorithms based on the Lanczos method are suitable because the main computation at each step, a matrix-vector product, can be efficiently performed by means of the fast Fourier transform exploiting the structure of the involved matrix. We compare the performance in terms of accuracy and efficiency of four algorithms: the classical SVD algorithm based on the QR decomposition, the Lanczos algorithm, the Lanczos algorithm with partial reorthogonalization, and the implicitly restarted Lanczos algorithm. Extensive simulation studies show that the latter two algorithms perform best.

Algorithms↗
PubMed2002

Effects of non-nutritive sucking on heart rate, respiration and oxygenation: a model-based signal processing approach.

Several studies support the idea that the use of pacifiers can reduce the risk of Sudden Infant Death Syndrome. To investigate the effect of non-nutritive sucking (NNS), we measured heart rate, abdominal respiration, EMG and arterial oxygen saturation of 20 neonates. Also, in 10 of these neonates, changes in cerebral hemoglobin concentrations were acquired by means of near-infrared spectroscopy. Using a parametric technique to model the heart rate as a sum of exponentially damped sinusoids, two main frequency components were found in the heart rate during NNS: a frequency of approximately 0.08 Hz due to the alternation of sucking bursts and pauses, and a frequency of approximately 0.8 Hz that reflects the influence of the respiration. Our analysis shows that it is the alternation of bursts and pauses itself that causes the increased heart rate variability, and that this is not due to increased effort. This suggests that the neuronal mechanism regulating NNS also stimulates the heart rate. From our measurements, no effect of NNS on cerebral or peripheral oxygenation could be found. Furthermore, we show that our model-based signal processing technique is well suited for the analysis of non-stationary biomedical signals.

Electrocardiography↗
PubMed2002

Ultrasonographic features of the endometrium and the ovaries in women on etonogestrel implant.

OBJECTIVE: To evaluate the ultrasound features of the endometrium and ovaries in women on etonogestrel implant, and to correlate these features with the bleeding pattern. METHODS: Observational study including 188 consecutive women presenting for follow-up transvaginal ultrasound examination after insertion of an etonogestrel implant contraceptive device. Thirty women had more than one follow-up examination. The bleeding pattern was considered abnormal if, in the last 3 months, there were more than five episodes of vaginal bleeding, or there was prolonged bleeding exceeding 14 consecutive days. RESULTS: At first follow-up examination, the mean age was 29.7 years and 47% of women had an abnormal bleeding pattern. Most bleeding episodes were of less intensity than menses. The mean endometrial thickness (ET) on ultrasound was 2.9 mm (standard deviation, 2.0). Ovarian follicle growth exceeding 5 mm was observed in 60% of the cases. Ovulation was demonstrated in one woman. Univariate analysis showed a positive association (P < 0.01) between ET, bleeding pattern, and bleeding intensity. Follicle growth was positively associated (P < 0.01) with ET, bleeding pattern, and interval between insertion and examination. Multivariate analysis showed that the ET was on average 1.25 mm greater in women with abnormal bleeding (P = 0.0001). The odds of finding follicle growth were 2.8 times higher (95% confidence interval, 1.2-6.2) in women presenting with a three-layer type of endometrial morphology. There was no association between the other patients' characteristics and the bleeding pattern. CONCLUSIONS: Abnormal uterine bleeding in women on etonogestrel implant was associated with follicle growth and a thicker, three-layer type of endometrium, suggesting incomplete ovarian inhibition and estrogen stimulation of the endometrium.

Adolescent↗
PubMed2002

Color Doppler and gray-scale ultrasound evaluation of the postpartum uterus.

OBJECTIVES: To evaluate the color Doppler and gray-scale sonographic appearance of the uterus after pregnancy, with special attention to the occurrence of areas of enhanced vascularity and placental remnants. PATIENTS AND METHODS: Cross-sectional observational study involving 385 consecutive women presenting at their first visit after pregnancy. The uterus was evaluated using ultrasound with color Doppler. In the presence of placental remnants, blood was sampled for measurement of beta human chorionic gonadotropin (beta hCG), hemoglobin (Hb) and infectious parameters. If indicated, a dilatation and curettage was performed. RESULTS: In 8.3% of women, areas of enhanced vascularity were detected with color Doppler examination. Most cases (68.9%) were focal areas of one or more vessels. In 2.6% of patients abnormal vascularity extended over a large area of the whole myometrium. In 6.75% of cases, placental remnants were detected. In 46% of these, blood sampling revealed beta hCG levels below 30 mIU/mL; serological infection parameters and Hb concentration were within the normal range. CONCLUSIONS: Areas of enhanced vascularity of the uterus, ranging from a focal vascular pedicle to a larger area of the myometrium, are relatively common after pregnancy. They are predominantly seen in the presence of placental remnants, in the early postpartum period and after instrumental or manual delivery of the placenta. There are no clear risk factors for retained placental tissue, besides the history of blood transfusion in the early postpartum, and perhaps multigravidity. Serology is of little help in the diagnosis of retained gestational products. The knowledge of the ultrasound and color Doppler features of the uterus after pregnancy may prove of practical value for the management of abnormal uterine bleeding in the postpartum period.

Adolescent↗
PubMed2002

Cerebral tissue oxygenation index in very premature infants.

AIM: To describe normal values of the cerebral tissue oxygenation index (TOI) in premature infants. METHODS: TOI was measured by spatially resolved spectroscopy in preterm infants on the first 3 days of life. Infants with an abnormal cranial ultrasound were excluded. Other simultaneously measured variables were PaO(2), PaCO(2), pH, mean arterial blood pressure, heart rate, haemoglobin, glycaemia, and peripheral oxygen saturation. RESULTS: Fifteen patients with a median postmenstrual age of 28 weeks were measured. There was a significant increase in median TOI over the first 3 days of life: 57% on day 1, 66.1% on day 2, and 76.1% on day 3. Multiple regression analysis showed no correlation between TOI and postmenstrual age, peripheral oxygen saturation, mean arterial blood pressure, PaO(2), PaCO(2), and haemoglobin concentration. CONCLUSION: Cerebral TOI increases significantly in the first 3 days of life in premature babies. This increase probably reflects the increase in cerebral blood flow at this time.

Analysis of Variance↗
PubMed2001

Identification of a novel A20-binding inhibitor of nuclear factor-kappa B activation termed ABIN-2.

The nuclear factor kappaB (NF-kappaB) plays a central role in the regulation of genes implicated in immune responses, inflammatory processes, and apoptotic cell death. The zinc finger protein A20 is a cellular inhibitor of NF-kappaB activation by various stimuli and plays a critical role in terminating NF-kappaB responses. The underlying mechanism for NF-kappaB inhibition by A20 is still unknown. A20 has been shown to interact with several proteins including tumor necrosis factor (TNF) receptor-associated factors 2 and 6, as well as the inhibitory protein of kappaB kinase (IKK) gamma protein. Here we report the cloning and characterization of ABIN-2, a previously unknown protein that binds to the COOH-terminal zinc finger domain of A20. NF-kappaB activation induced by TNF and interleukin-1 is inhibited by overexpression of ABIN-2. The latter also inhibits NF-kappaB activation induced by overexpression of receptor-interacting protein or TNF receptor-associated factor 2. In contrast, NF-kappaB activation by overexpression of IKKbeta or direct activators of the IKK complex, such as Tax, cannot be inhibited by ABIN-2. These results indicate that ABIN-2 interferes with NF-kappaB activation upstream of the IKK complex and that it might contribute to the NF-kappaB-inhibitory function of A20.

Adaptor Proteins, Signal Transducing↗
PubMed2001

Functional redundancy of the zinc fingers of A20 for inhibition of NF-kappaB activation and protein-protein interactions.

The tumor necrosis factor (TNF) inducible protein A20 is a potent inhibitor of nuclear factor-kappaB (IkappaB)-mediated gene expression in response to TNF and several other stimuli. The C-terminal domain of A20 is characterized by seven zinc finger structures. Here, we show that a minimum of four zinc fingers is required to inhibit TNF-induced nuclear factor-kappaB (NF-kappaB) activation to a level that is comparable to that obtained with the wild-type A20 protein. However, there was no strict requirement for a particular zinc finger structure, since a mutant A20 protein containing only the first four zinc fingers was as potent as a mutant protein containing only the last four zinc fingers. A similar functional redundancy of the A20 zinc fingers was also observed for binding of A20 to a number of other proteins, including two novel NF-kappaB inhibitory proteins (ABIN-1, ABIN-2), A20 itself, the anti-apoptotic protein TXBP151, and a regulatory component of the IkappaB kinase complex, IKKgamma. Moreover, we demonstrate that complete loss of binding of any of these proteins correlates with complete loss of A20's ability to inhibit TNF-induced NF-kappaB activation. However, binding of IKKgamma as such is not sufficient for inhibition of NF-kappaB dependent gene expression in response to TNF.

Amino Acid Sequence↗