Abstract
Modeling switching processes for control purpose takes advantage from the Piece-Wise Affine identification of hybrid dynamical systems briefly recalled in this paper. A couple of applications are addressed, namely to discriminate hormone pulses from background noise, in a physiologically switching process, and to identify sleep apneas, as pathological switching among healthy and potentially risky states. Other potential applications are proposed.
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Babiloni, F., F. Carducci, S. Cerutti, D. Liberati, P. Rossini, A. Urbano, and C. Babiloni. Comparison between human and ANN detection of Laplacian-derived electroencephalographic activity related to unilateral voluntary movements. Comput. Biomed. Res. 33:59–74, 2000. doi:10.1006/cbmr.1999.1529.
Baraldi, P., A. A. Manginelli, M. Maieron, D. Liberati, and C. A. Porro. An ARX model-based approach to trial by trial identification of fMRI-BOLD responses. NeuroImage 37:189–201, 2007. doi:10.1016/j.neuroimage.2007.02.045.
Barron, A., J. Rissanen, and B. Yu. The minimum description length principle in coding and modelling. IEEE Trans. Inf. Theory 44:2743–2760, 1998. doi:10.1109/18.720554.
Bittanti, S., F. A. Cuzzola, F. Lorito, and G. Poncia. Compensation of nonlinearities in a current transformer for the reconstruction of the primary current. IEEE Trans. Control Syst. Technol 9(4):565–573, 2001. doi:10.1109/87.930967.
Bosin, A., N. Dessì, D. Liberati, and B. Pes. Learning Bayesian classifiers from gene-expression microarray data. Lecture Notes in Computer Science. New York: Springer-Verlag, vol. 3849, pp. 297–304. doi:10.1007/11676935_37.
DeNicolao, G., D. Liberati, and A. Sartorio. Stimulated secretion of pituitary hormones in normal humans: a novel direct assessment from blood concentrations. Ann. Biomed. Eng. 28:1131–1145, 2000.
Drago, G. P., E. Setti, L. Licitra, and D. Liberati. Forecasting the performance status of head and neck cancer patient treatment by an interval arithmetic pruned perceptron. IEEE Trans. Biomed. Eng. 49(8):782–787, 2002.
Ferrari-Trecate, G., M. Muselli, D. Liberati, and M. Morari. A clustering technique for the identification of piecewise affine systems. Automatica 39:205–217, 2003. doi:10.1016/S0005-1098(02)00224-8.
Garatti, S., S. Bittanti, D. Liberati, and P. Maffezzoli. An unsupervised clustering approach for leukemia classification based on DNA micro-arrays data. Intell. Data Anal. 11(2):175–188, 2007.
de Jong, H., J.-L. Gouzé, C. Hernandez, M. Page, T. Sari, and J. Geiselmann. Qualitative simulation of genetic regulatory networks using piecewise-linear models. Bull. Math. Biol. 66(2):301–340, 2004. doi:10.1016/j.bulm.2003.08.010.
Liberati, D., M. Cursi, T. Locatelli, G. Comi, and S. Cerutti. Total and partial coherence of spontaneous and evoked EEG by means of multi-variable autoregressive processing. Med. Biol. Eng. Comput. 35(2):124–130, 1997. doi:10.1007/BF02534142.
Locatelli, T., M. Cursi, D. Liberati, M. Franceschi, and G. Comi. EEG coherence in Alzheimer’s disease. Electroencephalogr. Clin. Neurophysiol. 106(3):229–237, 1998. doi:10.1016/S0013-4694(97)00129-6.
Muselli, M., and D. Liberati. Binary rule generation via hamming clustering. IEEE Trans. Knowl. Data Eng. 14(6):1258–1268, 2002. doi:10.1109/TKDE.2002.1047766.
Orizio, C., D. Liberati, C. Locatelli, D. DeGrandis, and A. Veicsteinas. Surface mechanomyogram reflects muscle fibres twitches summation. J. Biomech. 29(4):475–481, 1996. doi:10.1016/0021-9290(95)00063-1.
Pagani, M., G. Mazzuero, A. Ferrari, D. Liberati, S. Cerutti, D. Vaitl, L. Tavazzi, and A. Malliani. Sympatho-vagal interaction during mental stress: a study employing spectral analysis of heart rate variability in healthy controls and in patients with a prior myocardial infarction. Circulation 83(4), II43–II51, 1991.
Sartorio, A., G. De Nicolao, and D. Liberati. An improved computational method to assess pituitary responsiveness to secretagogue stimuli. Eur. J. Endocrinol. 147(3):323–332, 2002. doi:10.1530/eje.0.1470323.
Selim, S. Z., and M. A. Ismail. K-means-type algorithms: a generalized convergence theorem and characterization of local optimality. IEEE Trans. Pattern Anal. Mach. Intell. 6(1):81–86, 1984. doi:10.1109/TPAMI.1984.4767478.
Vapnik, V. Statistical Learning Theory. New York: Wiley, 1998.
Acknowledgment
Giancarlo Ferrari-Trecate is warmly thanked for being involved also in the physiological data analysis.
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Liberati, D. Biomedical Applications of Piece-Wise Affine Identification for Hybrid Systems. Ann Biomed Eng 37, 1871–1876 (2009). https://doi.org/10.1007/s10439-009-9750-x
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DOI: https://doi.org/10.1007/s10439-009-9750-x