Epileptic seizure prediction using phase synchronization based on bivariate empirical mode decomposition
Epileptic seizure prediction using phase synchronization based on bivariate empirical mode decomposition
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DOI：
10.1016/j.clinph.2013.09.047
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发表时间：
2014-06
影响因子：
4.7
通讯作者：
Yang Zheng;Gang Wang;Kuo Li;G. Bao;Jue Wang
中科院分区：
文献类型：
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作者：
Yang Zheng;Gang Wang;Kuo Li;G. Bao;Jue Wang
癫痫是一种常见的神经系统疾病，具有不可预测性。有效的癫痫发作预测算法对于难治性癫痫患者具有重要意义。首先，采用二维经验模态分解（BEMD）和希尔伯特变换相结合的方法对颅内脑电（EEG）记录进行瞬时相位检测。然后，相位信息被用来计算平均相位相干性（MPC）作为不同通道的EEG记录之间的相位耦合强度的度量。最后，发作前MPC时间进程的变化被用来提高癫痫发作警报。我们比较了所提出的方法与其他现有的方法，以进一步探讨其effectiveness.ResultsBoth的增加和减少的相位同步被发现癫痫发作前。我们的研究结果表明，该方法具有最好的性能在三个predictors.ConclusionsThe算法可以有效地提取相位同步性的变化之前，癫痫发作的预测和贡献的应用。显着基于BEMD方法的相位同步分析可能是一个有用的算法，在癫痫预测的临床应用。
ObjectiveEpilepsy is a common neurological disorder with unpredictability. An effective algorithm for seizure prediction is important for the patients with refractory epilepsy.MethodsWe proposed a seizure prediction method based on the phase synchronization information of neuronal electrical activities. Firstly, the instantaneous phase of the intracranial electroencephalograph (EEG) recordings was detected by the combination of bivariate empirical mode decomposition (BEMD) and Hilbert transformation. Then, the phase information was used to calculate the mean phase coherence (MPC) as a measure of phase coupling strength between different channels of EEG recordings. In the end, the preictal changes of MPC time courses were used to raise the seizure alarms. We compared the proposed method with other existing methods to further investigate its effectiveness.ResultsBoth the increase and the decrease of phase synchronization were found prior to seizure onset. Our results indicated that the proposed method had the best performance among three predictors.ConclusionsThe proposed algorithm can effectively extract the phase synchrony changes prior to the seizure onset and contribute to the application of the seizure prediction.SignificancePhase synchronization analysis based on the BEMD method may be a useful algorithm for clinical application in epileptic prediction.