癫痫脑电信号符号动力学分析

Symbolic Dynamics Analysis of Epileptic EEG Signals of the Rat

癫痫是大脑神经元异常放电所引起的常见神经系统疾病,其发作具有突然性和反复性特点,因此,提前预测发作以便对患者及时采取措施具有重要意义。本文引入符号动力学方法分析癫痫大鼠失神性发作时脑电(EEG)信号的特性,并对影响符号统计量的关键参数的选取进行讨论,计算癫痫发作不同时期EEG信号的符号熵和时间不可逆转性。研究发现正常发作间隙期,符号熵和时间不可逆转性指标值较大;从发作间隙期向发作期的转化阶段,即发作前期,二者明显减小;发作时维持较低水平。研究结果表明符号动力学方法能够揭示癫痫EEG动力学特征变化,符号熵和时间不可逆转性两个指标是表征癫痫发作不同阶段的敏感特征量,具有重要的潜在临床应用价值。

Epilepsy is a common chronic neurological disease, which is caused by excessive brain neuron discharge.The epileptic seizure has the characteristic of abruptness and reiteration. Prediction of seizures has great significance for patients to take timely and effective clinical measures. The symbolic dynamics method was introduced to analyze absence epilepsy EEG. The key parameters affecting the symbolic statistical quantities were discussed. The symbolic entropy and time irreversebility were calculated in different epilepsy stages. It was found that the symbolic entropyand the time irreversebility were rather big in interictal stage. The two parameters declined significantly during the transformation process from interictal stage to ictal stage and maintained lower value during ictal stage. The results showed that the symbolic dynamics method could reflect the changes of epilepsy EEG. The symbolic entropy and time irreversebility are sensitive features indicating different stages of seizures and have potential important clinical applications.