Focal onset seizure prediction using convolutional networks
H Khan, L Marcuse, M Fields… - IEEE Transactions on …, 2017 - ieeexplore.ieee.org
IEEE Transactions on Biomedical Engineering, 2017•ieeexplore.ieee.org
Objective: This paper investigates the hypothesis that focal seizures can be predicted using
scalp electroencephalogram (EEG) data. Our first aim is to learn features that distinguish
between the interictal and preictal regions. The second aim is to define a prediction horizon
in which the prediction is as accurate and as early as possible, clearly two competing
objectives. Methods: Convolutional filters on the wavelet transformation of the EEG signal
are used to define and learn quantitative signatures for each period: interictal, preictal, and …
scalp electroencephalogram (EEG) data. Our first aim is to learn features that distinguish
between the interictal and preictal regions. The second aim is to define a prediction horizon
in which the prediction is as accurate and as early as possible, clearly two competing
objectives. Methods: Convolutional filters on the wavelet transformation of the EEG signal
are used to define and learn quantitative signatures for each period: interictal, preictal, and …
Objective
This paper investigates the hypothesis that focal seizures can be predicted using scalp electroencephalogram (EEG) data. Our first aim is to learn features that distinguish between the interictal and preictal regions. The second aim is to define a prediction horizon in which the prediction is as accurate and as early as possible, clearly two competing objectives.
Methods
Convolutional filters on the wavelet transformation of the EEG signal are used to define and learn quantitative signatures for each period: interictal, preictal, and ictal. The optimal seizure prediction horizon is also learned from the data as opposed to making an a priori assumption.
Results
Computational solutions to the optimization problem indicate a 10-min seizure prediction horizon. This result is verified by measuring Kullback-Leibler divergence on the distributions of the automatically extracted features.
Conclusion
The results on the EEG database of 204 recordings demonstrate that (i) the preictal phase transition occurs approximately ten minutes before seizure onset, and (ii) the prediction results on the test set are promising, with a sensitivity of 87.8% and a low false prediction rate of 0.142 FP/h. Our results significantly outperform a random predictor and other seizure prediction algorithms.
Significance
We demonstrate that a robust set of features can be learned from scalp EEG that characterize the preictal state of focal seizures.
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