Modelling Biomedical Signals

Analysis and models of biomedical data by theoretical physics methods: the cluster variation method for approximate reasoning in medical diagnosis, H.J. Kappen; analysis of EEG in epilepsy, K. Lehnertz et al; stochastic approaches to modelling of physiological rhythms, P.Ch. Ivanov and C.-C. Lo; chaotic parameters in time series of ECG, respiratory movements and arterial pressure, E. Conte and A. Federici; computer analysis of acoustic respiratory signals, A. Vena et al; the immune System - B cell binding to multivalent antigen, G. Bhanot; stochastic models of immune system ageing, L. Mariani et al. Neural networks and neurosciences: artificial neural networks in neuroscience, N. Accornero and M. Capzza; biological neural networks - modelling and measurements, R. Stoop and S. Lecchini; selectivity property of a class of energy based learning rules in presence of noisy signals, A. Bazzani et al; pathophysiology of schizophrenia - fMRI and working memory, G. Blasi and A. Bertolino; ANN for electrophysiological analysis of neurological diseases, R. Bellotti et al; detection of multiple sclerosis lesions in MRIs with neural networks, P. Blonda et al; monitoring respiratory mechanics using artificial neural networks, G. Perchiazzi et al. Genomics and molecular biology: cluster analysis of DNA-chip data, E. Domany; clustering mtDNA sequences for human evolution studies, C. Marangi et al; finding regulatory sites from statistical analysis of nucleotide frequencies in the upstream region of eukaryotic genes, M. Caselle et al; regulation of early growth response-1 gene expression and signalling mechanisms in neuronal cells - physiological stimulation and stress, G. Cibelli; geometrical aspects of protein folding, C. Micheletti; the physics of motor proteins, G. Lattanzi and A. Maritan; phasing proteins - experimental loss of information and its recovery, C. Giacovazzo et al.