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Sabine Van Huffel

Sabine Van Huffel
KU Leuven | ku leuven · Department of Electrical Engineering (ESAT)

About

916
Publications
137,692
Reads
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29,213
Citations
Citations since 2017
57 Research Items
11250 Citations
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201720182019202020212022202305001,0001,500
201720182019202020212022202305001,0001,500
Introduction
Full professor in Biomedical Data Processing

Publications

Publications (916)
Article
Full-text available
Background Recent studies explored the relationship between early brain function and brain morphology, based on the hypothesis that increased brain activity can positively affect structural brain development and that excitatory neuronal activity stimulates myelination. Objective To investigate the relationship between maturational features from ea...
Conference Paper
Early life stress in the neonatal intensive care unit (NICU) predisposes premature infants to adverse health outcomes. Although those patients experience frequent apneas and sleep-wake disturbances during their hospital stay, clinicians still rely on clinical scales to assess pain and stress burden. This study addresses the relationship between str...
Conference Paper
In this paper we explore the use of updated tensor decompositions for the monitoring of brain hemodynamics in neonates. For this study, we used concomitant measurements of heart rate, mean arterial blood pressure, arterial oxygen saturation, EEG, and brain oxygenation - measured using near-infrared spectroscopy. These measurements were obtained fro...
Article
Previous MRI and proton spectroscopy ( ¹ H-MRS)studies have revealed impaired neuronal integrity and altered neurometabolite concentrations in the motor cortex of patients with amyotrophic lateral sclerosis (ALS). Here, we aim to use MRI with conventional and novel MRS sequences to further investigate neurometabolic changes in the motor cortex of A...
Chapter
Identification and localization of brain tumor tissues plays an important role in diagnosis and treatment planning of gliomas. A fully automated superpixel wise two-stage tumor tissue segmentation algorithm using random forest is proposed in this paper. First stage is used to identify total tumor and the second stage to segment sub-regions. Feature...
Article
Full-text available
Amyotrophic Lateral Sclerosis (ALS) is an incurable neurodegenerative disease primarily characterized by progressive degeneration of motor neurons in the motor cortex, brainstem and spinal cord. Due to relatively fast progression of ALS, early diagnosis is essential for possible therapeutic intervention and disease management. To identify potential...
Article
Full-text available
Objective . We develop a method for automated four-state sleep classification of preterm and term-born babies at term-age of 38-40 weeks postmenstrual age (the age since the last menstrual cycle of the mother) using multichannel Electroencephalogram (EEG) recordings. At this critical age, EEG differentiates from broader Quiet Sleep (QS) and Active...
Article
Full-text available
A wearable electroencephalogram (EEG) device for continuous monitoring of patients suffering from epilepsy would provide valuable information for the management of the disease. Currently no EEG setup is small and unobtrusive enough to be used in daily life. Recording behind the ear could prove to be a solution to a wearable EEG setup. This article...
Article
Objective: Automated seizure detection in the home environment has attracted increasing interest in recent decades. Heart rate-based seizure detection is a way to detect temporal lobe epilepsy seizures at home, but patient-independent classifiers have been shown to be insufficiently accurate. This is due to the high patient-dependence of heart rat...
Article
Full-text available
[This corrects the article DOI: 10.1186/s41512-016-0001-y.].
Article
Background: Fragmented QRS (fQRS) on a 12-lead ECG has been linked with adverse outcome. However, the visual scoring of ECGs is prone to inter- and intra-observer variability. Methods: Five observers, two experienced and three novel, assessed fQRS in 712 digital ECGs, 100 were re-evaluated to assess intra-observer variability. Fleiss and Cohen's...
Preprint
Full-text available
Auditory attention detection (AAD) is promising for use in auditory-assistive devices to detect to which sound the user is attending. Being able to train subjects in achieving high AAD performance would greatly increase its application potential. In order to do so an acceptable temporal resolution and online implementation are essential prerequisit...
Chapter
Glioblastoma (GB) implies a devastating prognosis with an average survival of 14–16 months using the current standard of care treatment [1]. GB is the most frequent malignant tumour originating from the brain parenchyma, and it is characterised by a marked intratumoural heterogeneity, proneness to infiltrate throughout the brain parenchyma, robust...
Conference Paper
The objective of this paper is to classify Multiple Sclerosis courses using features extracted from Magnetic Resonance Spectroscopic Imaging (MRSI) combined with brain tissue segmentations of gray matter, white matter, and lesions. To this purpose we trained several classifiers, ranging from simple (i.e. Linear Discriminant Analysis) to state-of-th...
Article
Full-text available
In recent years, functional connectivity in the developmental science received increasing attention. Although it has been reported that the anatomical connectivity in the preterm brain develops dramatically during the last months of pregnancy, little is known about how functional and effective connectivity change with maturation. The present study...
Article
Full-text available
Automated analysis of the electroencephalographic (EEG) data for the brain monitoring of preterm infants has gained attention in the last decades. In this study, we analyze the complexity of neonatal EEG, quantified using multiscale entropy. The aim of the current work is to investigate how EEG complexity evolves during electrocortical maturation a...
Article
Full-text available
In neonatal intensive care units, there is a need for around the clock monitoring of EEG, especially for recognizing seizures. An automated seizure detector with an acceptable performance can partly fill this need. In order to develop a detector, an extensive dataset labeled by experts is needed. However, accurately defining neonatal seizures on EE...
Article
Aims QRS fragmentation (fQRS) has been proposed as a predictor of sudden cardiac death (SCD) and all-cause mortality in ischemic (ICM) and non-ischemic cardiomyopathy patients. However the value of fQRS in patients with a LVEF < 35% is a matter of debate. Methods All consecutive patients with an indication for an ICD in primary prevention of SCD w...
Article
Full-text available
Non-negative matrix factorization (NMF) has become a widely used tool for additive parts-based analysis in a wide range of applications. As NMF is a non-convex problem, the quality of the solution will depend on the initialization of the factor matrices. In this study, the successive projection algorithm (SPA) is proposed as an initialization metho...
Data
Software code. The software code used within the study has been made available in the file S1_File.zip, along with one patient’s anonymized dataset. Interested researchers may run the code on this examplary dataset. The code has been written in matlab. After unzipping the file, please consult the file README_Code.docx on how to run an NMF analysis...
Chapter
Sleep is a complex physiological process that plays a key role in maintaining homeostasis, well-being and overall health. It has an internal structure characterized by sleep stages, which is often affected by either the high demands of the current 24-h society or by different sleep disorders. In fact, these disturbances to the regular sleep structu...
Article
Objective: T wave alternans (TWA) is a promising non-invasive risk stratification tool for sudden cardiac death which can be detected from surface ECG. This paper proposes a novel method to automatically detect TWA based on tensor decomposition methods. Approach: Two different tensor decomposition approaches are examined and compared, namely can...
Article
Full-text available
Purpose: The purpose of this study is classifying multiple sclerosis (MS) patients in the four clinical forms as defined by the McDonald criteria using machine learning algorithms trained on clinical data combined with lesion loads and magnetic resonance metabolic features. Materials and Methods: Eighty-seven MS patients [12 Clinically Isolated Syn...
Data
(A–D) MS groups comparison: Disease age vs. Cho/Cre.
Chapter
This study investigates the relationship between brain oxygenation, assessed by means of near infrared spectroscopy (NIRS), and brain function, assessed by means of electroencephalography (EEG). Using NIRS signals measuring the regional cerebral oxygen saturation (rScO2) and computing the fractional tissue oxygen extraction (FTOE), we compared how...
Conference Paper
Quantification of white matter lesion changes on brain magnetic resonance (MR) images is of major importance for the follow-up of patients with Multiple Sclerosis (MS). Many automated segmentation methods have been proposed. However, most of them focus on a single time point MR scan session and hence lack consistency when evaluating lesion changes...
Article
Objective To assess interrater agreement based on majority voting in visual scoring of neonatal seizures. Methods An online platform was designed based on a multicentre seizure EEG-database. Consensus decision based on ‘majority voting’ and interrater agreement was estimated using Fleiss’ Kappa. The influences of different factors on agreement wer...
Article
Neonatal sleep is a crucial state that involves endogenous driven brain activity, important for neuronal survival and guidance of brain networks. Sequential EEG-sleep analysis in preterm infants provides insights into functional brain integrity and can document deviations of the biologically pre-programmed process of sleep ontogenesis during the ne...
Conference Paper
This study investigates the multifractal formalism framework for quiet sleep detection in preterm babies. EEG recordings from 25 healthy preterm infants were used in order to evaluate the performance of multifractal measures for the detection of quiet sleep. Results indicate that multifractal analysis based on wavelet leaders is able to identify qu...
Conference Paper
Cardiac arrhythmia or irregular heartbeats are an important feature to assess the risk on sudden cardiac death and other cardiac disorders. Automatic classification of irregular heartbeats is therefore an important part of ECG analysis. We propose a tensor-based method for single- and multi-channel irregular heartbeat classification. The method ten...
Conference Paper
Full-text available
In neonatal intensive care units performing continuous EEG monitoring, there is an unmet need for around-the-clock interpretation of EEG, especially for recognizing seizures. In recent years, a few automated seizure detection algorithms have been proposed. However, these are suboptimal in detecting brief-duration seizures (<; 30s), which frequently...
Article
Full-text available
Background Segmentation of gliomas in multi-parametric (MP-)MR images is challenging due to their heterogeneous nature in terms of size, appearance and location. Manual tumor segmentation is a time-consuming task and clinical practice would benefit from (semi-) automated segmentation of the different tumor compartments. Methods We present a semi-a...
Article
Full-text available
Sleep state development in preterm neonates can provide crucial information regarding functional brain maturation and give insight into neurological well being. However, visual labeling of sleep stages from EEG requires expertise and is very time consuming, prompting the need for an automated procedure. We present a robust method for automated dete...
Article
Automated seizure detection in a home environment has been of increased interest the last couple of decades. The electrocardiogram is one of the signals that is suited for this application. In this paper, a new method is described that classifies different heart rate characteristics in order to detect seizures from temporal lobe epilepsy patients....
Conference Paper
Joris de Groot, Christiana Naaktgeboren, Hans Reitsma, Carl Moons Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, 3584 CG Utrecht, Netherlands Correspondence: Joris de Groot (j.degroot-17@umcutrecht.nl) A major contributor to the rising problem of overdiagnosis, with the subsequent risk of overtreatment, is t...
Article
Full-text available
Purpose: Magnetic resonance spectroscopic imaging (MRSI) provides complementary information to conventional magnetic resonance imaging. Acquiring high resolution MRSI is time consuming and requires complex reconstruction techniques. Methods: In this paper, a patch-based super-resolution method is presented to increase the spatial resolution of meta...
Article
Full-text available
Purpose: The purpose of this paper is discriminating between tumor progression and response to treatment based on follow-up multi-parametric magnetic resonance imaging (MRI) data retrieved from glioblastoma multiforme (GBM) patients. Materials and Methods: Multi-parametric MRI data consisting of conventional MRI (cMRI) and advanced MRI [i.e., perfu...
Article
Full-text available
One of the important aspects to be considered in rheumatic and musculoskeletal diseases is the patient’s activity capacity (or performance), defined as the ability to perform a task. Currently, it is assessed by physicians or health professionals mainly by means of a patient-reported questionnaire, sometimes combined with the therapist’s judgment o...
Article
Full-text available
Purpose: Lesion volume is a meaningful measure in multiple sclerosis (MS) prognosis. Manual lesion segmentation for computing volume in a single or multiple time points is time consuming and suffers from intra and inter-observer variability. Methods: In this paper, we present MSmetrix-long: a joint expectation-maximization (EM) framework for two ti...
Article
Proton Magnetic Resonance Spectroscopic Imaging (¹H MRSI) has shown great potential in tumor diagnosis since it provides localized biochemical information discriminating different tissue types, though it typically has low spatial resolution. Magnetic Resonance Imaging (MRI) is widely used in tumor diagnosis as an in vivo tool due to its high resolu...
Article
Full-text available
Clinical data is comprised by a large number of synchronously collected biomedical signals that are measured at different locations. Deciphering the interrelationships of these signals can yield important information about their dependence providing some useful clinical diagnostic data. For instance, by computing the coupling between Near-Infrared...
Article
Full-text available
Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) record a mixture of ongoing neural processes, physiological and nonphysiological noise. The pattern of interest, such as epileptic activity, is often hidden within this noisy mixture. Therefore, blind source separation (BSS) techniques, which can retrieve the activity pat...
Article
Full-text available
Problem setting Support vector machines (SVMs) are very popular tools for classification, regression and other problems. Due to the large choice of kernels they can be applied with, a large variety of data can be analysed using these tools. Machine learning thanks its popularity to the good performance of the resulting models. However, interpreting...
Data
Bivariate plot of the Iris data (training data). (PDF)
Data
Definition of the different contributions in the approximation of the SVM classifiers. This appendix summarizes how the terms f(q) and f(p,q) used in the expansion of the SVM model, are calculated for the linear, polynomial and RBF kernel. (PDF)
Data
Setting of artificial examples. This appendix illustrates the settings of the artificial examples. (PDF)
Data
Explanation of how a color based nomogram results from a risk prediction model. This appendix explains in detail how a risk prediction model that can be represented by means of Eq (3) can be represented by the proposed color based nomogram. (PDF)
Data
Application of the method to the IRIS data set. This video illustrates the possibilities of the R package by means of an R application using the IRIS data set. (MP4)
Article
Full-text available
Tumor segmentation is a particularly challenging task in high-grade gliomas (HGGs), as they are among the most heterogeneous tumors in oncology. An accurate delineation of the lesion and its main subcomponents contributes to optimal treatment planning, prognosis and follow-up. Conventional MRI (cMRI) is the imaging modality of choice for manual seg...
Article
Clinical risk prediction models are increasingly being developed and validated on multicenter datasets. In this article, we present a comprehensive framework for the evaluation of the predictive performance of prediction models at the center level and the population level, considering population-averaged predictions, center-specific predictions, an...
Conference Paper
QRS fragmentation is visible in the ECG signal as the presence of one or more deflections, notches or slurs in the QRS complex. The presence of QRS fragmentation is strongly related with the myocardial fibrosis or scarrings and has been associated with adverse outcome in patients. Since detection of fragmented QRS complexes is mainly done on a visu...
Conference Paper
It is well-known that sleep apnea affects the respiration and the heart rate (HR), and studies have shown that the cardiorespiratory coupling is also compromised during obstructive sleep apnea (OSA). Furthermore, the classification of hypopneas is challenging, in particular when only ECG-derived features are used. In this context, this study invest...
Article
Processing of longitudinal diffusion tensor imaging (DTI) data is a crucial challenge to better understand pathological mechanisms of complex brain diseases such as multiple sclerosis (MS) where white matter (WM) fiber-bundles are variably altered by inflammatory events. In this work, we propose a new fully automated method to detect longitudinal c...