Integrative science: biosignal processing and modeling.
Résumé
Coupling computational modeling and information processing in biology and medicine is a major challenge for better comprehending structures and functions of living systems. Signal processing should extract the relevant information required to explore complex organization levels, at all space and time scales. Advances coming from applied physics and mathematics are challenged by extremely hot topics in biology and medicine. The biomedical scene has proven to be the most difficult to address due to the fact that biomedical processes involve nonGaussian, nonlinear, and nonstationary components. This paper provides some clues on processing schemes such as time and frequency transforms, blind signal separation, independent component analysis, empirical mode decomposition, particle methods and Kernel methods that may help in lessening the ambiguity about the observed components of the mixtures to be handled and, this way, facilitating their matching with models.
Mots clés
blind source separation
independent component analysis
medical signal processing
physiological models
time-frequency analysis
Kernel methods
applied physics
biology
biomedical processes
biosignal processing
blind signal separation
complex organization levels
computational modeling
empirical mode decomposition
frequency transform
information processing
integrative science
living systems
mathematics
medicine
nonGaussian processes
nonlinear processes
nonstationary processes
particle methods
space scale
time scale
time transform