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Prediction of epilepsy seizure from multi-channel electroencephalogram by effective connectivity analysis using Granger causality and directed transfer function methods

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Abstract

Epilepsy is a chronic disorder, which causes strange perceptions, muscle spasms, sometimes seizures, and loss of awareness, associated with abnormal neuronal activity in the brain. The goal of this study is to investigate how effective connectivity (EC) changes effect on unexpected seizures prediction, as this will authorize the patients to play it safe and avoid risk. We approve the hypothesis that EC variables near seizure change significantly so seizure can be predicted in accordance with this variation. We introduce two time-variant coefficients based on standard deviation of EC on Freiburg EEG dataset by using directed transfer function and Granger causality methods and compare index changes over the course of time in five different frequency bands. Comparison of the multivariate and bivariate analysis of factors is implemented in this investigation. The performance based on the suggested methods shows the seizure occurrence period is approximately 50 min that is expected onset stated in, the maximum value of sensitivity approaching ~ 80%, and 0.33 FP/h is the false prediction rate. The findings revealed that greater accuracy and sensitivity are obtained by the designed system in comparison with the results of other works in the same condition. Even though these results still are not sufficient for clinical applications. Based on the conclusions, it can generally be observed that the greater results by DTF method are in the gamma and beta frequency bands.

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Notes

  1. Granger Causality Effective Connectivity Index.

  2. Directed Transfer Function Effective Connectivity Index.

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Acknowledgements

We are immensely grateful to all experts for their assessments and opinions on the manuscript, although any errors are our own and should not tarnish the reputations of these esteemed professionals. The authors are very grateful to the Epilepsy Center of the University Hospital of Freiburg, Germany, for their consent to use the invasive EEG recordings in this work.

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Authors and Affiliations

  1. Department of Biomedical Engineering, Islamic Azad University, Science and Research Branch, Tehran, Iran

    Mona Hejazi

  2. Department of Biomedical Engineering, Faculty of Biomedical Engineering, Shahed University, Tehran, Iran

    Ali Motie Nasrabadi

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  1. Mona Hejazi
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  2. Ali Motie Nasrabadi
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Correspondence to Mona Hejazi.

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Hejazi, M., Motie Nasrabadi, A. Prediction of epilepsy seizure from multi-channel electroencephalogram by effective connectivity analysis using Granger causality and directed transfer function methods. Cogn Neurodyn 13, 461–473 (2019). https://doi.org/10.1007/s11571-019-09534-z

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