Epileptic seizure prediction based on dynamic Bayesian networks

Luben Han; Yanli Zhang

doi:10.1117/12.2687813

28 August 2023 Epileptic seizure prediction based on dynamic Bayesian networks

Luben Han, Yanli Zhang

Proceedings Volume 12724, Second International Conference on Biomedical and Intelligent Systems (IC-BIS 2023); 1272413 (2023) https://doi.org/10.1117/12.2687813
Event: Second International Conference on Biomedical and Intelligent Systems (IC-BIS2023), 2023, Xiamen, China

Abstract

In order to meet the accuracy and real-time requirements of epileptic seizure prediction, a prediction method based on Dynamic Bayesian Network (DBN) is proposed in this paper. After a pre-processing of EEG recordings, the permutation entropy features are calculated and selected to establish DBN model. The structure leaning and parameter learning of DBN are carried out respectively based on scoring-search algorithm and maximum likelihood estimation algorithm. Finally, seizure prediction is realized through probabilistic inference of DBN. Evaluated on CHB-MIT EEG dataset, the proposed prediction method achieved an average sensitivity of 91.4% and false alarm rate of 0.14/h, with an average prediction time of 35.64 min. Experiment results demonstrate the good performance of DBN in seizure prediction.

Citation Download Citation

Luben Han and Yanli Zhang "Epileptic seizure prediction based on dynamic Bayesian networks", Proc. SPIE 12724, Second International Conference on Biomedical and Intelligent Systems (IC-BIS 2023), 1272413 (28 August 2023); https://doi.org/10.1117/12.2687813

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