• Yu-xin Zheng, Jun-ming Zhu, Yu Qi, Xiao-xiang Zheng, and Jian-min Zhang.
• Department of Neurosurgery, The Second Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China.
• Neuromodulation. 2015 Feb 1;18(2):79-84; discussion 84.

ObjectiveThis study presents a multichannel patient-specific seizure detection method based on the empirical mode decomposition (EMD) and support vector machine (SVM) classifier.Materials And MethodsThe EMD is used to extract features from intracranial electroencephalography (EEG). A machine-learning algorithm is used as a classifier to discriminate between seizure and nonseizure intracranial EEG epochs. A postprocessing algorithm is proposed to reject artifacts and increase the robustness of the method. The proposed method was evaluated using 463 hours of intracranial EEG recordings from 17 patients with a total of 51 seizures in the Freiburg EEG database.ResultsThe proposed method had better performance than most of the existing seizure detection systems, including an average sensitivity of 92%, false detection rate (FDR) of 0.17/hour, and time delay (TD) of 12 sec. Moreover, the FDR could be further reduced by a TD extension.ConclusionsGiven its high sensitivity and low FDR, the proposed patient-specific seizure detection method can greatly assist clinical staff with automatically marking seizures in long-term EEG or detecting seizure onset online with high performance. Early and accurate seizure detection using this method may serve as a practical tool for planning epilepsy interventions.© 2014 International Neuromodulation Society.

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