Biomedical Data Processing Group
The Biomedical Data Processing Group investigates the use of signal processing and data fusion techniques for the analysis, and classification of biomedical signals and data, with special emphasis on ECG, EMG, EEG signals, as well as CT-SCAN, RMN and PET-SCAN images.
We have recently worked on artefact removal using signal processing techniques, on the analysis of polysomnographic signals, on voice pathology detection, on emboly detection from CT-SCAN images, and tumor segmentation.
We have established collaborations with several university hospitals : CHU Vésale, CHU Tivoli, CHU Mont-Godinne, CHU Erasme, CHU Saint-Luc.
Applications : sleep analysis (RW DREAMS project), medical diagnosis assistance for laryngologists (RW ECLISPSE project), computer-bases surgery assistance for anesthesists (RW TANIA project), computer-aided diagnosis of pulmonary embolism (RW iMed project), radiotherapy planning and monitoring tools (RW Mercator project).
The team is multidisciplinary. Some of us have previously worked in speech processing, image processing, biomedical signal processing, data fusion, medical imagery, and in the diagnosis and treatment of patients with speech related complaints.
Current R&D projects
The ROBIGame project (2014 - 2017)
Past R&D projects
The COMPTOUX project (2010 - 2013)
Brain-Computer Interfaces for Ambulatory Applications (2009 - 2014) - PhD Thesis Matthieu Duvinage
Disabilities affecting mobility, in particular, often lead to exacerbated isolation and thus fewer communication opportunities, resulting in a limited participation in social life. Additionally, as costs for the health-care system can be huge, rehabilitation-related devices and lower-limb prostheses (or orthoses) have been intensively studied so far. However, although many devices are now available, they rarely integrate the direct will of the patient. Indeed, they basically use motion sensors or the residual muscle activities to track the next move.
Therefore, to integrate a more direct control from the patient, Brain-Computer Interfaces (BCIs) are here proposed and studied under ambulatory conditions. Basically, a BCI allows you to control any electric device without the need of activating muscles. In this work, the conversion of brain signals into a prosthesis kinematic control is studied following two approaches. First, the subject transmits his desired walking speed to the BCI. Then, this high-level command is converted into a kinematics signal thanks to a Central Pattern Generator (CPG)-based gait model, which is able to produce automatic gait patterns. Our work thus focuses on how BCIs do behave in ambulatory conditions. The second strategy is based on the assumption that the brain is continuously controlling the lower limb. Thus, a direct interpretation, i.e. decoding, from the brain signals is performed. Here, our work consists in determining which part of the brain signals can be used.
The CALLAS project (2007 - 2010)
CALLAS ("Conveying Affectiveness in Leading-Edge Living Adaptive Systems") is a European Integrated Project (FP6). It aims at designing and developing multimodal architectures giving a strong importance to emotions, for Arts and Entertainment.
The global idea of the project is that New Medias, targeting recognition and production of emotions, can enhance users' (or spectators') experience and interaction. CALLAS is thus investigating how, at the input level, emotions can be detected and how, at the output level, these emotions can be processed to generate a new audiovisual content enriching users' experience. The input modalities include both vocal and body languages (recorded through video cameras and haptic devices). In order to improve the recognition of emotions, the problem of merging the information coming from these different modalities will also be examined. The applications are ranging from digital theatre productions (playing an audio or visual content in relation with the actors' and spectators' feelings) to real or virtual museum tours (taking the visitor's interest into account to reshape the exposition and select the level of information its audioguide will give), without forgetting interactive television (modifying a scenario according to the spectator's emotions).
The ECLIPSE project (2006 - 2012)
There are various methods of analysis aiming at classifying vocal pathologies, but none is really powerful. First of all, the "perceptive" analysis makes it possible to the doctor to qualify the quality of the voice according to several criteria, the problem of this method being subjectivity of the judgement. That's why specialists prefer the "acoustic" analysis, computer-assisted method consisting in calculating on the vocal signal a series of objective parameters which are used to qualify the voice of the patient. But this method is only effective to analyze supported vowels, and thus not continuous speech, what would be more suitable. Moreover, the strongly hoarse speakers are unable to produce pseudoperiodic speech.
The ECLIPSE project aims to develop software of acoustic analysis for any type of voice and any degree of hoarseness. The project implements the simultaneous analysis of the vocal signals and the images of the vibration of the vocal cords and aims, in addition to the realization of a clinical prototype, the realization of a portable device intended to ensure a follow-up of the patients at the risk on their workplace.
The TANIA project (2006 - 2009)
In the frame of the TANIA project, we aim at designing a decision support tool for the anesthesiologists. The research involves diverse fields of applied mathematics, in particular data mining and signal processing techniques.
Thèse Thomas Dubuisson (2006 - 2011) - Glottal Source Estimation and Automatic Detection of Dysphonic Speakers
This thesis is devoted to the development of methods for detecting the dysphonic speakers. The pathological aspects of these phonations are usually assessed in clinics by means of perceptive and objective analysis. In support to this assessment, there is a need to develop new objective methods in order to detect a pathology or evaluate the voice quality before and after surgery. After a large overview of existing methods in terms of features and classification approaches and a comparison between different methodologies for the features selection, it is investigated to which extent a limited number of features can be combined in a simple classification approach to detect the presence of a pathology. A first application shows that the correlation between acoustic descriptors, which do not require the estimation of fundamental period, is able to discriminate well between normal and pathological sustained vowels. A second application shows the interest of combining the information extracted from the speech signal and the estimation of the glottal source for the detection of voice pathologies. In this application, two features (one computed on the speech signal and the other on the glottal contribution) are selected by means of mutual information-based measure and their distribution for normal and pathological voices is estimated to derive a simple classifier based on Gaussian Mixture Models. The ability of this classification approach to discriminate between normal and pathological sustained vowels is demonstrated and it is proposed to nuance the decision provided by the classifier by including indetermination zones in the normal/pathological decision. These precautions allow to increase the reliability of the decision provided to the clinician.
The DREAMS project (2003 - 2008)
Sleep scoring is essential for the detection of sleep pathologies in hospitals. It is usually performed manually by visual inspection of polysomnograms (PSG : EEG+EMG+EOG, mainly). Automated techniques exist, but fail to provide reliable results for pathological sleep.
The DREAMS project precisely aims at producing automated sleep scoring techniques in case of sleep pathologies.
The iMed project (2003 - 2006)
The iMed project is about the design of a method to automatically detect emboli in the vessel tree of the pulmonary artery, from HCT (helicoidal computed tomography) millimeter slices.
The MERCATOR project (2003 - 2007)
In the context of preoperative images visualization and computer-assisted surgical planning, the Mercator project aims at updating the plannings made
before the operation by integrating real-time information resulting from intra-operative events in order to readjust the plans and the initial data on
the real evolution during the operation or the radiotherapy.
The SIMILAR project (2003 - 2007)
The SIMILAR European Network of Excellence will create an integrated task force on multimodal interfaces that respond intelligently to speech, gestures, vision, haptics and direct brain connections by merging into a single research group excellent European laboratories in Human-Computer Interaction (HCI) and in Signal Processing.
SIMILAR will develop a common theoretical framework for fusion and fission of multimodal information using the most advanced Signal Processing tools constrained by Human Computer Interaction rules.
SIMILAR will develop a network of usability test facilities and will establish an assessment methodology.
SIMILAR will develop a common distributed software platform available for researchers and the public at large through www.openinterface.org
SIMILAR will address Grand Challenges in the field of edutainment, interfaces for disabled people and interfaces for medical applications.
SIMILAR will establish a top-level foundation which will manage an International Journal, Special Sessions in existing conferences, organize summer schools, interact with key European industrial partners and promote new research activities at the European level.
TCTS Lab's contibution will be on Grand Challenges related to TTS and ASR technologies, and their integration into a multimodal framework. We will also work on enhancing Brain Computer Interfaces. SIMILAR is considered a central project for the evolution of our lab.
ATTENTION (2003 - 2007) - PhD Thesis Matei Mancas
Attention is a simplification or filtering process which transforms a huge acquired unstructured data set into a smaller structured one while preserving the main information. All cognitive processes need attention; humans pay attention (consciously or unconsciously) from their birth to their death in every single moment. Attention is even used during the dreams and the R.E.M. (Rapid Eye Movements) sleep phase.
Nevertheless, attention is not specifically a human process but it is simply used by any living being from humans to insects.
Attention is the beginning of intelligence: there is no intelligence without attention!
Similarly to the fact that attention is the beginning of intelligence in biology, computational attention may be the starting point of artificial intelligence in engineering applications.
Computational attention provides machines with human-like reactions and behaviours and let them free to make decisions even in unexpected situations:
- A computer which pays attention is able to be surprised and interested in novel data.
- A computer which pays attention is able to understand novel situations and to choose the important data it will learn.
Thèse S. DEVUYST (2003 - 2011) - Automatic Analysis of Polysomnographic Traces from Adults
Cette thèse a été réalisée en collaboration avec le laboratoire de sommeil de l’hôpital André Vésale de Charleroi et a pour objectif l’analyse automatique des signaux du sommeil des adultes.
Plus spécifiquement, son but est, d’une part, de détecter un ensemble de micro-événements apparaissant dans certains états de sommeil, ou caractéristiques à certaines pathologies (comme les apnées du sommeil), et d’autre part, de discerner automatiquement les différents stades de sommeil.
Pour ce faire, une attention toute particulière a été portée au traitement des artefacts. Une méthode originale de correction des interférences cardiaques sur les électroencéphalogrammes a notamment été mise au point. En outre, les procédés de classification automatiques en stades du sommeil ont été revus de manière à s’adapter aux nouvelles règles de cotation en stades du sommeil de l’AASM (l’American Academy of Sleep Medicine). Enfin, plusieurs procédés de classification ont été comparés en évaluant leurs résultats de détection sur une même base de données de 47 enregistrements polysomnographiques de nuits complètes.
EMBOLI (2002 - 2007) - PhD Thesis Raphael Sebbe
Pulmonary embolism (PE) is an extremely common and highly lethal
condition that is a leading cause of death in all age groups. Over the
past 10 years, computed tomography (CT) scanners have gained
acceptance as a minimally invasive method for diagnosing PE. In this
book, a framework for computer-aided diagnosis of PE in contrast-
enhanced CT images is presented. It consists of a combination of a
method for segmenting the pulmonary arteries (PA), emboli detection
methods as well as a scheme for evaluating their performances. The
segmentation of the PA serves one of the clot detection methods, and
is carried out through a region growing method that makes use of a
priori knowledge of vessel topology. Two different approaches for clot
detection are proposed: the first one performs clot detection by
analyzing the concavities in the segmentation of the pulmonary
arterial tree. It works in a semi-automatic way and it enables the
detection of thrombi in the larger sections of the PA. The second
method does not make use of PA segmentation and is thus fully
automatic, enabling detection of clots farther in the vessels. The
combination of these methods provides a robust detection technique
that can be used as a safeguard by radiologists, or even as
preliminary computer-aided diagnosis (CAD) tool. The evaluation of the
method is also discussed, and a scheme for measuring its performance
is proposed, including a practical approach to making reference
detection data, or ground truths, by radiologists.
Director of the Sleep Laboratory
|tel : 071/92.14.57
Senior Researcher, PhD
TCTS Lab - FPMs
|tel : +32 65 37 47 43
Researcher, PhD Student
TCTS Lab - FPMs
|tel : +32 65 37 47 26
Senior Researcher, PhD
TCTS Lab - FPMs
|tel : +32 65 37 47 71