Biomedical Data Processing Group

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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).

Projects

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 CALLAS project (2007 - )

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 PAST project (2007 - )

PAST stands for Pathology Assessment by Source-Tract separation of speech. Speech is one of the most natural way to communicate among humans and can be affected by some troubles when used in an intensive way. Specially, this kind of problems affect people like singers or teachers. When the pathology becomes painful, these persons have to undercome a speech assessment performed by a clinician. This examination consists of acoustical, aerodynamic and image recordings which help the clinician to diagnose the degree of pathology. In the field of speech processing, most researchers have been interested in estimating contributions of the glottal source and the vocal tract in the speech signal. Among these, the ZZT representation was recently proposed and suggest very interesting perspectives. This PhD thesis proposes to use this representation and other ones in order to evaluate the impact of pathology by the estimation of the glottal source and the vocal tract contributions in speech signal.

The ECLIPSE project (2006 - )

There are various methods of analysis aiming at classifying vocal pathologies, but none is really powerful. First of all, the â€śperceptiveâ€ť 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. Thatâ€™s why specialists prefer the â€śacousticâ€ť 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 - )

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.

The DREAMS project (2003 - )

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 SLEEP project (2002 - )

Sleep pathologies affect nearly 30 percent of the population, involving serious consequences on people's behaviour, vigilance and health. Traditionally, sleep analysis is done by an expert by analysing the polysomnographic signals (brain activity-EEG, eye movements-EOG, skeletal muscle activation-EMG, heart rhythm-ECG, etc). As this task is very time consuming and tedious, some automated processing procedures have been developed. In the framework of the DREAMS project, our study aims to improve the current automatic methods of sleep stages detection. Among several technical issues, the detection and elimination of artefacts require a particular attention. Independent component analysis and adaptive signal processing are some techniques used for their detection and extraction. This PhD thesis (S. Devuyst, supervisor: Prof. T. Dutoit) also proposes to automatically detect some transient sleep events, like sleep apnea, periodic limb movements, sleep spindles, etc.

Past R&D projects

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 ATTENTION project (2003 - 2007)

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.

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.

The EMBOLI project (2002 - 2007)

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.