Joining the benefits: Combining epileptic seizure prediction methods

Authors

  • Hinnerk Feldwisch-Drentrup,

    1. Bernstein Center for Computational Neuroscience Freiburg, University of Freiburg, Freiburg, Germany
    2. Freiburg Center for Data Analysis and Modeling, University of Freiburg, Freiburg, Germany
    3. Epilepsy Center, University Hospital of Freiburg, Freiburg, Germany
    4. Department of Neurobiology and Biophysics, Faculty of Biology, University of Freiburg, Freiburg, Germany
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  • Björn Schelter,

    1. Freiburg Center for Data Analysis and Modeling, University of Freiburg, Freiburg, Germany
    2. Department of Physics, University of Freiburg, Freiburg, Germany
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  • Michael Jachan,

    1. Bernstein Center for Computational Neuroscience Freiburg, University of Freiburg, Freiburg, Germany
    2. Freiburg Center for Data Analysis and Modeling, University of Freiburg, Freiburg, Germany
    3. Epilepsy Center, University Hospital of Freiburg, Freiburg, Germany
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  • Jakob Nawrath,

    1. Freiburg Center for Data Analysis and Modeling, University of Freiburg, Freiburg, Germany
    2. Epilepsy Center, University Hospital of Freiburg, Freiburg, Germany
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  • Jens Timmer,

    1. Bernstein Center for Computational Neuroscience Freiburg, University of Freiburg, Freiburg, Germany
    2. Freiburg Center for Data Analysis and Modeling, University of Freiburg, Freiburg, Germany
    3. Department of Physics, University of Freiburg, Freiburg, Germany
    4. Freiburg Institute for Advanced Studies, University of Freiburg, Freiburg, Germany
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  • Andreas Schulze-Bonhage

    1. Bernstein Center for Computational Neuroscience Freiburg, University of Freiburg, Freiburg, Germany
    2. Epilepsy Center, University Hospital of Freiburg, Freiburg, Germany
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Address correspondence to Hinnerk Feldwisch-Drentrup, FDM, Eckerstr. 1, 79104 Freiburg, Germany. E-mail: feldwisch@bccn.uni-freiburg.de; mail@hinnerkf.de

Summary

Purpose:  In recent years, a variety of methods developed in the field of linear and nonlinear time series analysis have been used to obtain reliable predictions of epileptic seizures. Because individual methods for seizure prediction so far have shown statistical significance but insufficient performance for clinical applications, we investigated possible improvements by combining algorithms capturing different aspects of electroencephalogram (EEG) dynamics.

Methods:  We applied the mean phase coherence and the dynamic similarity index to long-term continuous intracranial EEG data. The predictive performance of both methods was assessed and statistically evaluated separately, as well as by using logical “AND” and “OR” combinations.

Results:  Used independently, either method resulted in a statistically significant prediction performance in only a few patients. Particularly the “AND” combination led to improved prediction performances, leading to an increase in sensitivity and/or specificity. For a maximum false prediction rate of 0.15/h, the mean sensitivity improved from about 25% for the individual methods to 43.2% for the “AND” and to 35.2% for the “OR” combination.

Discussion:  This study shows that combinations of prediction methods are promising new approaches to enhance seizure prediction performance considerably. It allows merging the individual benefits of prediction methods in a complementary manner. Because either sensitivity or specificity of seizure prediction methods can be improved depending on the needs of the desired clinical application, the combination opens a new window for future use in a clinical setting.

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