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| Epileptic Seizure Prediction Based on Probabilistic Discriminative Extreme Leaning Machine |
1 Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116023,China
2 Department of Neurology, First Affiliated Hospital of Dalian Medical University, Dalian 116011, China |
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Abstract Sensitivity and falsepositive rate are two of the most important indicators of epileptic seizure prediction. When the sensitivity is improved, the falsepositive rate will increase at the same time. To solve this problem, an epileptic seizure prediction method based on probabilistic discriminative extreme leaning machine (PDELM) was proposed. The method utilized PDELM for classification after extracting features from EEG by using mean phase coherence (MPC). And the probability of each class could be obtained. Then the balance of the sensitivity and the false-positive rate was maintained by determining a threshold. At last, after smoothing by a filter, the prediction results was obtained. Simulations on the 21 intractable epilepsy patients showed that the proposed method not only has a superior prediction performance (the mean sensitivity can reach 80.4% and the mean false-positive rate was as low as 0.10 h-1), but also required a short training time, which provided a valuable reference for the clinical application of epileptic seizure prediction.
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| Fund:国家自然科学基金（61074096） |
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