CSDL Home IEEE Transactions on Pattern Analysis & Machine Intelligence 2009 vol.31 Issue No.09 - September

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Issue No.09 - September (2009 vol.31)

pp: 1537-1551

John A. Quinn , Makerere University, Kampala

Christopher K.I. Williams , University of Edinburgh, Edinburgh

Neil McIntosh , University of Edinburgh, Edinburgh

ABSTRACT

Condition monitoring often involves the analysis of systems with hidden factors that switch between different modes of operation in some way. Given a sequence of observations, the task is to infer the filtering distribution of the switch setting at each time step. In this paper, we present factorial switching linear dynamical systems as a general framework for handling such problems. We show how domain knowledge and learning can be successfully combined in this framework, and introduce a new factor (the “X-factor”) for dealing with unmodeled variation. We demonstrate the flexibility of this type of model by applying it to the problem of monitoring the condition of a premature baby receiving intensive care. The state of health of a baby cannot be observed directly, but different underlying factors are associated with particular patterns of physiological measurements and artifacts. We have explicit knowledge of common factors and use the X-factor to model novel patterns which are clinically significant but have unknown cause. Experimental results are given which show the developed methods to be effective on typical intensive care unit monitoring data.

INDEX TERMS

Condition monitoring, switching linear dynamical system, switching Kalman filter, novelty detection, intensive care.

CITATION

John A. Quinn, Christopher K.I. Williams, Neil McIntosh, "Factorial Switching Linear Dynamical Systems Applied to Physiological Condition Monitoring",

*IEEE Transactions on Pattern Analysis & Machine Intelligence*, vol.31, no. 9, pp. 1537-1551, September 2009, doi:10.1109/TPAMI.2008.191REFERENCES

- [4] R. Morales-Menedez, N. de Freitas, and D. Poole, “Real-Time Monitoring of Complex Industrial Processes with Particle Filters,”
Advances in Neural Information Processing Systems 15, S. Becker, S.Thrun, and K. Obermayer, eds., MIT Press, 2002.- [5] U. Lerner, R. Parr, D. Koller, and G. Biswas, “Bayesian Fault Detection and Diagnosis in Dynamic systems,”
Proc. 17th Nat'l Conf. Artificial Intelligence, pp. 531-537, 2000.- [6] V. Pavlović, J. Rehg, and J. MacCormick, “Learning Switching Linear Models of Human Motion,”
Advances in Neural Information Processing Systems 13, T. Leen, T. Dietterich, and V. Tresp, eds., MIT Press, 2000.- [7] Y. Li, T. Wang, and H.-Y. Shum, “Motion Texture: A Two-Level Statistical Model for Character Motion Synthesis,”
Proc. ACM SIGGRAPH '02, pp. 465-472, 2002.- [10] J. Droppo and A. Acero, “Noise Robust Speech Recognition with a Switching Linear Dynamic Model,”
Proc. IEEE Int'l Conf. Acoustics, Speech, and Signal Processing, 2004.- [13] C. Williams, J. Quinn, and N. McIntosh, “Factorial Switching Kalman Filters for Condition Monitoring in Neonatal Intensive Care,”
Advances in Neural Information Processing Systems 18, Y.Weiss, B. Schölkopf, and J. Platt, eds., MIT Press, 2006.- [14] J. Quinn and C. Williams, “Known Unknowns: Novelty Detection in Condition Monitoring,”
Proc. Third Iberian Conf. Pattern Recognition and Image Analysis, J. Martí, J.-M. Benedí, A.M.Mendonça, and J. Serrat, eds., 2007.- [15] J. Quinn, “Neonatal Condition Monitoring Demonstration Code,” http://cit.ac.ug/jquinnsoftware.html, 2008.
- [16] K. Tsien, “Dynamic Bayesian Networks: Representation, Inference and Learning,” PhD dissertation, Univ. of California, Berkeley, 2002.
- [17] Z. Ghahramani and G. Hinton, “Parameter Estimation for Linear Dynamical Systems,” technical report, Dept. of Computer Science, Univ. of Toronto, 1996.
- [18] Z. Ghahramani and G. Hinton, “Variational Learning for Switching State-Space Models,”
Neural Computation, vol. 12, no. 4, pp.963-996, 1998.- [19] J. Candy,
Model-Based Signal Processing. Wiley-IEEE Press, 2005.- [20] P.C. Woodland, “Hidden Markov Models Using Vector Linear Prediction and Discriminative Output Distributions,”
Proc. IEEE Int'l Conf. Acoustics, Speech, and Signal Processing, vol. I, pp. 509-512, 1992.- [21] Z. Ghahramani and M. Jordan, “Factorial Hidden Markov Models,”
Machine Learning, vol. 29, pp. 245-273, 1997.- [22] M. West and J. Harrison,
Bayesian Forecasting and Dynamic Models. Springer, 1999.- [23] J. Quinn, “Bayesian Condition Monitoring in Neonatal Intensive Care,” PhD dissertation, Univ. of Edinburgh, http://www.era.lib. ed.ac.uk/handle/1842 1645, 2007.
- [25] J. Ma and S. Perkins, “Online Novelty Detection on Temporal Sequences,”
Proc. Ninth ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining, pp. 613-618, 2003.- [27] U. Lerner and R. Parr, “Inference in Hybrid Networks: Theoretical Limits and Practical Algorithms,”
Proc. 17th Ann. Conf. Uncertainty in Artificial Intelligence, pp. 310-318, 2001.- [28] K. Murphy, “Switching Kalman Filters,” technical report, Univ. of California, Berkeley, 1998.
- [29] K. Murphy and S. Russell, “Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks,”
Sequential Monte Carlo in Practice, A. Doucet, N. de Freitas, and N. Gordon, eds., Springer-Verlag, 2001.- [30] J. Hunter and N. McIntosh, “Knowledge-Based Event Detection in Complex Time Series Data,”
Proc. Joint European Conf. Artificial Intelligence in Medicine and Medical Decision Making, W. Horn, Y.Shahar, G. Lindberg, S. Andreassen, and J. Wyatt, eds., 1999.- [37] R. Shumway and D. Stoffer,
Time Series Analysis and Its Applications. Springer-Verlag, 2000.- [38] A. Spengler, “Neonatal Baby Monitoring,” master's thesis, School of Informatics, Univ. of Edinburgh, 2003.
- [39] J. Hunter, “TSNet—A Distributed Architecture for Time Series Analysis,”
Proc. Intelligent Data Analysis in bioMedicine and Pharmacology, pp. 85-92, 2006.- [40] D. Barber and B. Mesot, “A Novel Gaussian Sum Smoother for Approximate Inference in Switching Linear Dynamical Systems,”
Advances in Neural Information Processing Systems 18, Y. Weiss, B.Schölkopf, and J. Platt, eds., MIT Press, 2006. |