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Epileptic Seizure Detection Using Dynamical Preprocessing (STLmax)

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Quantitative Neuroscience

Part of the book series: Biocomputing ((BCOM,volume 2))

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Abstract

The long-term properties of the Short-term largest Lyapunov exponents (STLmax) of EEG have been used successfully in epilepsy seizure prediction. STLmax profiles also show short-term patterns characterizing seizures that can be used for detection purposes. In this paper, we explore two such properties shown by the STLmax data during seizures and develop and compare automatic seizure detection algorithms on over 1,000 hours of data.

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References

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Author information

Authors and Affiliations

  1. Computational NeuroEngineering Laboratory, Electrical and Computer Engineering, University of Florida, Gainesville, Florida, USA, 32611

    G. Ramachandran & J. C. Principe

  2. Brain Dynamics Laboratory, Neuroscience, University of Florida, Gainesville, Florida, USA, 32611

    J. C. Sackellares

Authors
  1. G. Ramachandran
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  2. J. C. Principe
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  3. J. C. Sackellares
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Editor information

Editors and Affiliations

  1. Departments of Industrial and Systems Engineering, Biomedical Engineering, and McKnight Brain Institute, University of Florida, USA

    P. M. Pardalos

  2. Departments of Neuroscience, Neurology, Pediatrics, and Biomedical Engineering, University of Florida; VA Medical Center, USA

    J. C. Sackellares

  3. Departments of Pediatrics, Neurology, and Neuroscience, University of Florida, USA

    P. R. Carney

  4. Department of Biomedical Engineering, Arizona State University, USA

    L. D. Iasemidis

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© 2004 Kluwer Academic Publishers

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Ramachandran, G., Principe, J.C., Sackellares, J.C. (2004). Epileptic Seizure Detection Using Dynamical Preprocessing (STLmax). In: Pardalos, P.M., Sackellares, J.C., Carney, P.R., Iasemidis, L.D. (eds) Quantitative Neuroscience. Biocomputing, vol 2. Springer, Boston, MA. https://doi.org/10.1007/978-1-4613-0225-4_9

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  • DOI: https://doi.org/10.1007/978-1-4613-0225-4_9

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4613-7951-5

  • Online ISBN: 978-1-4613-0225-4

  • eBook Packages: Springer Book Archive

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