M Gratkowski1, J Haueisen, L Arendt-Nielsen, A Cn Chen, F Zanow. 1. Institute of Biomedical Engineering and Informatics, Computer Science and Automation Faculty, Technische Universität Ilmenau, Gustav-Kirchhoff Str. 2, 98693 Ilmenau, Germany. Maciej.Gratkowski@tu-ilmenau.de
Abstract
OBJECTIVES: The purpose of this paper is to introduce a new method for spatial-time-frequency analysis of multichannel biomedical data. We exemplify the method for data recorded with a 31-channel Philips biomagnetometer. METHODS: The method creates approximations and decompositions of spatiotemporal signal distributions using elements (atoms) chosen from a very large and redundant set (dictionary). The method is based on the Matching Pursuit algorithm, but it uses atoms that are distributed both in time and space (instead of only time-distributed atoms in standard Matching Pursuit). The time-frequency distribution of signal components is described by Gabor atoms and their spatial distribution is modeled by spatial modes. The spatial modes are created with the help of Bessel functions. Two versions of the method, differing in the definition of spatial properties of the atoms, are presented. RESULTS: The technique was validated on simulated data and real magnetic field data. It was used for removal of powerline noise from multichannel magnetoencephalography data, extraction of high-frequency somatosensory evoked fields and for separation of partially overlapping T- and U-waves in magnetocardiography. CONCLUSIONS: The method allows for parameterization of multichannel data in the time-frequency as well as in the spatial domains. It thus can be used for signal preserving filtering simultaneously in time, frequency, and space. Applications are e.g. the elimination of artifact components, extraction of components with biological meaning, and data exploration.
OBJECTIVES: The purpose of this paper is to introduce a new method for spatial-time-frequency analysis of multichannel biomedical data. We exemplify the method for data recorded with a 31-channel Philips biomagnetometer. METHODS: The method creates approximations and decompositions of spatiotemporal signal distributions using elements (atoms) chosen from a very large and redundant set (dictionary). The method is based on the Matching Pursuit algorithm, but it uses atoms that are distributed both in time and space (instead of only time-distributed atoms in standard Matching Pursuit). The time-frequency distribution of signal components is described by Gabor atoms and their spatial distribution is modeled by spatial modes. The spatial modes are created with the help of Bessel functions. Two versions of the method, differing in the definition of spatial properties of the atoms, are presented. RESULTS: The technique was validated on simulated data and real magnetic field data. It was used for removal of powerline noise from multichannel magnetoencephalography data, extraction of high-frequency somatosensory evoked fields and for separation of partially overlapping T- and U-waves in magnetocardiography. CONCLUSIONS: The method allows for parameterization of multichannel data in the time-frequency as well as in the spatial domains. It thus can be used for signal preserving filtering simultaneously in time, frequency, and space. Applications are e.g. the elimination of artifact components, extraction of components with biological meaning, and data exploration.
Authors: Line Lindhardt Egsgaard; Laura Petrini; Giselle Christoffersen; Lars Arendt-Nielsen Journal: Exp Brain Res Date: 2011-10-25 Impact factor: 1.972
Authors: Line Lindhardt Egsgaard; Line Buchgreitz; Li Wang; Lars Bendtsen; Rigmor Jensen; Lars Arendt-Nielsen Journal: Exp Brain Res Date: 2011-11-02 Impact factor: 1.972