采样间隔依赖下癫痫信号的状态转移网络特征提取  

Sampling intervals dependent feature extraction for state transfer networks of epileptic signals

作　　者：张镭 阎爽 顾长贵 ZHANG Lei;YAN Shuang;GU Changgui(Department of Systems Science,Business School,University of Shanghai for Science and Technology,Shanghai 200093,P.R.China)

机构地区：[1]上海理工大学管理学院系统科学系,上海200093

出　　处：《生物医学工程学杂志》2024年第6期1128-1136,共9页Journal of Biomedical Engineering

基　　金：国家自然科学基金资助项目(12275179);上海市自然科学基金资助项目(21ZR1443900)。

摘　　要：癫痫发作期脑电与发作间期癫痫样放电具有近似的波形,有效提取癫痫发作的特征在理论和实践上至关重要。本文分别在多个采样间隔下,利用可见小图构建状态转移网络并挖掘网络特征发现:发作期在采样间隔变化的情况下更能维持特征波形,且采样间隔变化较小时的特征网络结构不易发生改变;而发作间期癫痫样放电的特征网络结构,则是在相对较大的采样间隔范围内不易改变;此外,发作期关键节点在时序上具有长程相关性,对调控系统行为起重要作用。本文研究还发现,对于500 Hz左右的立体定向脑电图而言,当采样间隔为0.032 s时,两者特征差异最大。综上,本文研究有效揭示了大脑系统病理变化特征与采样间隔之间的关联规律,这在临床诊断上对癫痫的识别、分类与预测具有潜在的应用价值。Epileptic seizures and the interictal epileptiform discharges both have similar waveforms.And a method to effectively extract features that can be used to distinguish seizures is of crucial importance both in theory and clinical practice.We constructed state transfer networks by using visibility graphlet at multiple sampling intervals and analyzed network features.We found that the characteristics waveforms in ictal periods were more robust with various sampling intervals,and those feature network structures did not change easily in the range of the smaller sampling intervals.Inversely,the feature network structures of interictal epileptiform discharges were stable in range of relatively larger sampling intervals.Furthermore,the feature nodes in networks during ictal periods showed long-term correlation along the process,and played an important role in regulating system behavior.For stereo-electroencephalography at around 500 Hz,the greatest difference between ictal and the interictal epileptiform occurred at the sampling interval around 0.032 s.In conclusion,this study effectively reveals the correlation between the features of pathological changes in brain system and the multiple sampling intervals,which holds potential application value in clinical diagnosis for identifying,classifying,and predicting epilepsy.

关 键 词：立体定向脑电图 可见小图 状态转移网络 模体 特征提取 

分 类 号：R742.1[医药卫生—神经病学与精神病学] TN911.7[医药卫生—临床医学]