基于非线性互依赖性的癫痫致痫区识别方法研究
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摘要
致痫区的准确识别是治疗癫痫并减少副作用的基础,但传统视觉识别的方法在很多情况并不能令人满意。信号处理方法可以获得一些视觉观察不能发现的信息来作为传统方法的补充。致痫区识别可以看作一个驱动方识别问题,为了解决这个问题提出一种非线性互依赖性测度来作为致痫区(驱动方)的标识。非线性互依赖性可以检测EEG信号之间耦合的强度和方向信息,特别是耦合的方向信息可用以揭示癫痫发作传播的方向。对于不同的应用,以k和a这两个参数调节非线性互依赖性的灵敏度和完备性。在神经群模型构建的平台上对所提出致痫区识别的方法进行仿真。仿真结果显示,对于兴奋程度不同的致痫区,在没有突触延迟的情况下可以取得98.84%的总识别率,在有突触延迟的情况下可以取得与无突触延迟情况相近的识别率,说明基于非线性互依赖性的致痫区识别可以适用于不同的致痫区类型。
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.
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.
引文
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[2]王玉,周卫东,李淑芳,等.脑电信号的分形截距特征分析及在癫痫检测中的应用[J].中国生物医学工程学报,2011,30(4):562-566.
[3]Wendling,F,Badier JM,Chauvel P et al.A method to quantify invariant information in depth-recorded epileptic seizures[J].Electroenceph Clin Neurophysiol,1997,102:472-485.
[4]曹嘉悦,封洲燕,郭哲杉,等.闭环式电刺激抑制痫样棘波发放的机制研究[J].中国生物医学工程学报,2016,35(1):79-87.
[5]马斌荣,杨虎.图像融合技术及其在癫痫患者中的应用[J].中国生物医学工程学报,2005,24(3):357-361.
[6]Van der Heyden MJ,Velis DN,Hoekstra BP,et al.Nonlinear analysis of intracranial human EEG in temporal lobe epilepsy[J].Clin Neurophysiol,1999,110(10):1726-1740.
[7]Le Van Quyen M,Martinerie J,Adam C,et al.Unstable periodic orbits in a human epileptic activity[J].Phys Rev E,1997,56:3401-3411.
[8]Lehnertz K,Elger CE.Spatio-temporal dynamics of the primary epileptogenic area in the temporal lobe epilepsy characterized by neuronal complexity loss[J].Electroencephalography and Clinical Neurophysiology,1995,95:108-117.
[9]Jansen BH,Zouridakis G,Brandt ME.A neurophysiologicallybased mathematical model of flash visual evoked potentials[J].Biol Cybern,1993,68(3):275-283.
[10]Jansen BH,Rit VG.Electroencephalogram and visual evoked potential generation in a mathematical model of coupled cortical columns[J].Biol Cybern,1995,73(4):357-366.
[11]Wendling F,Hernandez A,Bellanger JJ,et al.Interictal to ictal transition in human temporal lobe epilepsy:insights from a computational model of intracerebral EEG[J].J Clin Neurophysiol,2005,22(5):343-356
[12]David O,Friston KJ.A neural mass model for MEG/EEG:coupling and neuronal dynamics[J].Neuro Image,2003,20(3):1743-1755
[13]Wang J,Wang M,Li X,et al.Closed-loop control of epileptiform activities in a neural population model using a proportional-derivative controller[J].Chinese Physics B,2015,24(3):038701
[14]马震.基于神经群模型的致痫兴奋性控制研究[J].生物医学工程系杂志,2016,33(2):258-262.
[15]Buzsáki G.Large-scale recording of neuronal ensembles[J].Nat Neurosci,2004,7:446-451.
[16]Buzsáki G,Draguhn A.Neuronal oscillations in cortical networks[J].Science,2004,304:1926-1929.
[17]Bikson M,Fox JE,Jefferys Jr G.Neuronal aggregate formation underlies spatiotemporal dynamics of nonsynaptic seizure initiation[J].J Neurophysiol,2003,89:2330-2333.
[18]Fox JE,Bikson M,Jefferys Jr G.The effect of neuronal population size on the development of epileptiform discharges in the low calcium model of epilepsy[J].Neurosci Lett,2007,411:158-161.
[19]Niranjan C,Shivkumar S,Kostas T,et al.Controlling epileptic seizures in a neural mass model[J].J Comb Optim,2009,17:98-116.
[20]Zhen M,Weidong Z,Shujuan G,et al.Synchronization regulation in a model of coupled neural masses.Biological Cybernetics,2013,107(2):131-140.
[21]王刚,但炜,熊伟茗,等.头皮脑电高频振荡波对继发性癫痫致痫灶的定位价值[J].第三军医大学学报,2014,36(22):2317-2320.
[22]Heers M,Rampp S,Stefan H,et al.MEG-based identification of the epileptogenic zone in occult peri-insular epilepsy[J].Seizure,2012,21(2):128-133.