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An Optimal Control Approach to Seizure Detection in Drug-Resistant Epilepsy

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A Systems Theoretic Approach to Systems and Synthetic Biology I: Models and System Characterizations

Abstract

Hidden state transitions are frequent events in complex biological systems like the brain. Accurately detecting these transitions from sequential measurements (e.g., EEG, MER, EMG, etc.) is pivotal in several applications at the interface between engineering and medicine, like neural prosthetics, brain-computer interface, and drug delivery, but the detection methodologies developed thus far generally suffer from a lack of robustness. We recently addressed this problem by developing a Bayesian detection paradigm that combines optimal control and Markov processes. The neural activity is described as a stochastic process generated by a Hidden Markov Model (HMM) and the detection policy minimizes a loss function of both probability of false positives and accuracy (i.e., lag between estimated and actual transition time). The policy results in a time-varying threshold that applies to the a posteriori Bayesian probability of state transition and automatically adapts to each newly acquired measurement, based on the evolution of the HMM and the relative loss for false positives and accuracy. An application of the proposed paradigm to the automatic online detection of seizures in drug-resistant epilepsy subjects is here reported.

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Acknowledgments

S. Santaniello was supported by the US National Science Foundation Grant ECCS 1346888. S. V. Sarma was supported by the US National Science Foundation CAREER Award 1055560 and the Burroughs Welcome Fund CASI Award 1007274. The Bayesian optimal detection framework presented in Sect. 6.2 and Sect. 6.3 was developed in [80] with preliminary results in [79, 82]. The network analysis and the generalized linear model structure presented in Sect. 6.4 and Sect. 6.4.1 were developed in [75] with preliminary results in [77]. Preliminary results with subjects PT-03 and PT-04 were presented in [76].

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Authors and Affiliations

  1. Institute for Computational Medicine, Department of Biomedical Engineering, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD, 21218, USA

    Sabato Santaniello, Samuel P. Burns & Sridevi V. Sarma

  2. Department of Neurosurgery, Johns Hopkins School of Medicine, 600 North Wolfe Street, Baltimore, MD, 21287, USA

    William S. Anderson

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  1. Sabato Santaniello
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  2. Samuel P. Burns
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  4. Sridevi V. Sarma
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Correspondence to Sabato Santaniello .

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Editors and Affiliations

  1. Electrical and Computer Engineering, University of Minnesota, Minneapolis, Minnesota, USA

    Vishwesh V. Kulkarni

  2. Bioengineering, Imperial College, London, United Kingdom

    Guy-Bart Stan

  3. Biotechnology, Indian Institute of Technology Madras, Chennai, India

    Karthik Raman

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Santaniello, S., Burns, S.P., Anderson, W.S., Sarma, S.V. (2014). An Optimal Control Approach to Seizure Detection in Drug-Resistant Epilepsy. In: Kulkarni, V., Stan, GB., Raman, K. (eds) A Systems Theoretic Approach to Systems and Synthetic Biology I: Models and System Characterizations. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-9041-3_6

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