Abstract：Exact identification of the epileptogenic zone (EZ) is the basis of epilepsy treatments and helps to reduce side effects. The results of traditional visual methods for identifying the origin of seizures are unsatisfactory in some cases. Signal processing methods could extract substantial information to complement visual inspection in many ways. In this study, EZ identification is regarded as a driver identification problem, and a nonlinear interdependence measure is proposed as an EZ (driver) indicator. It can detect coupling strength and directionality information, especially coupling directionality which can indicate seizure propagation direction, from EEG signals. Two directionally coupled neural mass models are employed for simulation investigation. Two parameters (k and a) can adjust the sensitivity and completeness of proposed interdependence for different applications. Proposed EZ Identification method is also simulated in the context of neural mass models. Simulation results illustrate that proposed EZ identification method can be applied to EZ at different excitatory degree, and achieves an overall identification rate of 98.84% for several EZ types in the cases without synaptic delay and about the same identification rate in the cases with a synaptic delay.
马震. 基于非线性互依赖性的癫痫致痫区识别方法研究[J]. 中国生物医学工程学报, 2018, 37(3): 327-334.
Ma Zhen. Epileptogenic Zone Identification Based on Nonlinear Interdependence. Chinese Journal of Biomedical Engineering, 2018, 37(3): 327-334.
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