An Automatic Patient-Specific Seizure Onset Detection Method Using Intracranial Electroencephalography

作者：Zheng, Yu-xin; Zhu, Jun-ming; Qi, Yu; Zheng, Xiao-xiang; Zhang, Jian-min^*

来源：Neuromodulation, 2015, 18(2): 79-84.

DOI：10.1111/ner.12214

• Empirical mode decomposition
• epilepsy
• intracranial EEG
• seizure detection
• support vector machine

Objective: This study presents a multichannel patient-specific seizure detection method based on the empirical mode decomposition (EMD) and support vector machine (SVM) classifier. @@@ Materials and Methods: The 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. @@@ Results: The 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. @@@ Conclusions: Given 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.

• 出版日期2015-2
• 单位浙江大学