BIOTENSORS

Biomedical Data Fusion using Tensor based Blind Source Separation

Coordinatore	KATHOLIEKE UNIVERSITEIT LEUVEN    more Spiacenti, non ci sono informazioni su questo coordinatore. Contattare Fabio per maggiori infomrazioni, grazie.
Nazionalità Coordinatore	Belgium [BE]
Totale costo	2˙500˙000 €
EC contributo	2˙500˙000 €
Programma	FP7-IDEAS-ERC Specific programme: "Ideas" implementing the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007 to 2013)
Code Call	ERC-2013-ADG
Funding Scheme	ERC-AG
Anno di inizio	2014
Periodo (anno-mese-giorno)	2014-04-01   -   2019-03-31

 Partecipanti

#	participant	country	role	EC contrib. [€]
1	KATHOLIEKE UNIVERSITEIT LEUVEN  Organization address address: Oude Markt 13 city: LEUVEN postcode: 3000 contact info Titolo: Ms. Nome: Sofie Cognome: Heroes Email: send email Telefono: +32 16 32 99 79 Fax: +32 16 326515	BE (LEUVEN)	hostInstitution	2˙500˙000.00
2	KATHOLIEKE UNIVERSITEIT LEUVEN  Organization address address: Oude Markt 13 city: LEUVEN postcode: 3000 contact info Titolo: Prof. Nome: Sabine Jeanne A Cognome: Van Huffel Email: send email Telefono: +32 16 321703 Fax: +32 16 32 1970	BE (LEUVEN)	hostInstitution	2˙500˙000.00

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 Obiettivo del progetto (Objective)

'Summary: the quest for a general functional tensor framework for blind source separation

Our overall objective is the development of a general functional framework for solving tensor based blind source separation (BSS) problems in biomedical data fusion, using tensor decompositions (TDs) as basic core. We claim that TDs will allow the extraction of fairly complicated sources of biomedical activity from fairly complicated sets of uni- and multimodal data. The power of the new techniques will be demonstrated for three well-chosen representative biomedical applications for which extensive expertise and fully validated datasets are available in the PI’s team, namely: • Metabolite quantification and brain tumour tissue typing using Magnetic Resonance Spectroscopic Imaging, • Functional monitoring including seizure detection and polysomnography, • Cognitive brain functioning and seizure zone localization using simultaneous Electroencephalography-functional MR Imaging integration.

Solving these challenging problems requires that algorithmic progress is made in several directions: • Algorithms need to be based on multilinear extensions of numerical linear algebra. • New grounds for separation, such as representability in a given function class, need to be explored. • Prior knowledge needs to be exploited via appropriate health relevant constraints. • Biomedical data fusion requires the combination of TDs, coupled via relevant constraints. • Algorithms for TD updating are important for continuous long-term patient monitoring. The algorithms are eventually integrated in an easy-to-use open source software platform that is general enough for use in other BSS applications.

Having been involved in biomedical signal processing over a period of 20 years, the PI has a good overview of the field and the opportunities. By working directly at the forefront in close collaboration with the clinical scientists who actually use our software, we can have a huge impact.'