Zare Meysam, Nazari Milad, Shojaei Amir, Raoufy Mohammad Reza, Mirnajafi-Zadeh Javad
Department of Physiology, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran.
Department of Technology, Electrical Engineering, Sharif University, Tehran.
Iran J Basic Med Sci. 2020 Feb;23(2):173-177. doi: 10.22038/IJBMS.2019.38722.9183.
Seizure detection during online recording of electrophysiological parameters is very important in epileptic patients. In the present study, online analysis of field potential recordings was used for detecting spontaneous seizures in epileptic animals.
Epilepsy was induced in rats by pilocarpine injection. During the chronic period of the pilocarpine model, local field potential (LFP) recording was run for at least 24 hr. At the same time, video monitoring of the animals was done to determine the real time of seizure occurrence. Both power and sample entropy of LFP were used for online analysis.
Obtained results showed that changes in LFP power are a better index for seizure detection. In addition, when we used one hundred consecutive epochs (each epoch equals 10 ms) of LFP for data analysis, the best detection was achieved.
It may be suggested that power is a suitable parameter for online analysis of LFP in order to detect the spontaneous seizures correctly.
在癫痫患者的电生理参数在线记录过程中，癫痫发作检测非常重要。在本研究中，利用场电位记录的在线分析来检测癫痫动物的自发性癫痫发作。
通过注射毛果芸香碱诱导大鼠癫痫发作。在毛果芸香碱模型的慢性期，进行至少24小时的局部场电位（LFP）记录。同时，对动物进行视频监测以确定癫痫发作的实际时间。LFP的功率和样本熵均用于在线分析。
所得结果表明，LFP功率变化是癫痫发作检测的更好指标。此外，当我们使用连续100个LFP时段（每个时段等于10毫秒）进行数据分析时，实现了最佳检测效果。
可以认为，功率是LFP在线分析的合适参数，以便正确检测自发性癫痫发作。
