On estimating phenomenological model states for epileptic seizure prediction

  1. Aamer Iqbal Bhatti

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Abstract

The prediction of epileptic seizure, like the disease itself, is a very old but largely unresolved problem. The prediction may greatly improve the quality of life for an epileptic patient. A low-cost measurement like an Electroencephalogram (EEG) involves the non-invasive monitoring of the brain voltage signals to detect epileptic seizures. This study aims to find ways to estimate the internal states of the neuron population by looking at the measured EEG signals so that the seizure onset may be predicted in advance. If one may estimate the states of the neural population, then by relating to the bifurcation horizon, one may find the seizure onset time. To find such states, one needs an estimator/observer of a neuronal state space model. Most of the neuronal models, be it biological or phenomenological, are non-linear. If a linear or any other approximation is used for the observer design, the bifurcation horizon may not be accurate enough. The biological models of neural population have the barrier of determining all the physiological parameters of a patient, which may be a bit limiting. A phenomenological neuron model, like Epileptor, is adapted, which is a non-linear and discontinuous model; estimating its states may help in finding the bifurcation parameters. However, the non-linearities are of Lipschitz and monotonic class. Using Linear Matrix Inequality solutions, a Lipschitz Non-linear model-based Observer is developed and tested in simulation, without using approximations of any kind. The simulation shows high fidelity of the observer to the model at hand, estimating the states, and helping in determining the bifurcation parameters.

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  1. Version published to 10.3389/fams.2026.1709607
  2. PREreview

    This Zenodo record is a permanently preserved version of a Structured PREreview. You can view the complete PREreview at https://prereview.org/reviews/15102348.

    Does the introduction explain the objective of the research presented in the preprint? Yes
    Are the methods well-suited for this research? Highly appropriate
    Are the conclusions supported by the data? Highly supported
    Are the data presentations, including visualizations, well-suited to represent the data? Somewhat appropriate and clear
    How clearly do the authors discuss, explain, and interpret their findings and potential next steps for the research? Somewhat clearly
    Is the preprint likely to advance academic knowledge? Highly likely
    Would it benefit from language editing? No
    Would you recommend this preprint to others? Yes, it's of high quality
    Is it ready for attention from an editor, publisher or broader audience? Yes, after minor changes

    Competing interests

    The author declares that they have no competing interests.

  3. PREreview

    This Zenodo record is a permanently preserved version of a Structured PREreview. You can view the complete PREreview at https://prereview.org/reviews/15079237.

    Does the introduction explain the objective of the research presented in the preprint? Yes
    Are the methods well-suited for this research? Somewhat appropriate
    Are the conclusions supported by the data? Somewhat supported
    Are the data presentations, including visualizations, well-suited to represent the data? Somewhat appropriate and clear
    How clearly do the authors discuss, explain, and interpret their findings and potential next steps for the research? Very clearly
    Is the preprint likely to advance academic knowledge? Somewhat likely
    Would it benefit from language editing? No
    Would you recommend this preprint to others? Yes, but it needs to be improved
    Is it ready for attention from an editor, publisher or broader audience? Yes, after minor changes

    Competing interests

    The author declares that they have no competing interests.

  4. Version published to 10.1101/2025.03.09.642227 on bioRxiv