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癫痫的典型特征是神经元群体产生异常同步发放,在记录的神经电信号中呈现为痫样棘波。为了定量分析癫痫发生时的痫样棘波,本文设计了一种基于窗口的检测法用于自动检测大鼠海马CA1区急性癫痫模型的棘波信号,即群峰电位(PS),并计算其特征参数。实验结果表明,在钾离子通道拮抗剂4-氨基吡啶(4-AP)和γ-氨基丁酸A型受体拮抗剂印防己毒素(PTX)诱导的癫痫模型中,该算法可直接从原始宽频带记录信号中正确识别PS波。两种模型中的PS检出率分别为94.2%±1.6%(n=11)和95.9%±1.9%(n=12),且误检率分别为3.5%±2.3%(n=11)和4.8%±2.3%(n=12),远小于普通阈值法的误检率。比较4-AP和PTX模型的PS波特征,结果显示:4-AP诱导的PS波具有较宽的波形,发放较分散,发放间隔主要分布于100~700 ms范围内。而PTX诱导的PS则呈现爆发式发放,发放率较高,发放间隔主要分布于2~20 ms范围内,使得每秒PS幅值之和显著大于4-AP模型。因此,PTX模型的神经元群体同步发放活动比4-AP模型要强烈。总之,该棘波检测新算法可以正确识别和分析痫样棘波,为癫痫发生机制的研究和癫痫治疗新方法的评估提供了一种有用的数据分析工具。
A typical feature of epilepsy is that abnormal distribution of neurons occurs synchronously, presenting as epileptiform spikes in the recorded neuroelectrical signals. In order to quantitatively analyze epileptic spike waves in epileptic seizures, a window-based detection method was designed for the automatic detection of spike signals (PS) in the rat hippocampal CA1 acute epilepsy model Characteristic Parameters. The experimental results show that in the model of epilepsy induced by potassium channel blockers 4-aminopyridine (4-AP) and γ-aminobutyric acid type A receptor antagonist picrotoxin (PTX), the algorithm can directly extract from the original broadband With the recorded signal correctly identify the PS wave. The detection rates of PS in the two models were 94.2% ± 1.6% (n = 11) and 95.9% ± 1.9% (n = 12), respectively, and the false positive rates were 3.5% ± 2.3% 4.8% ± 2.3% (n = 12), much less than the false detection rate of the normal threshold method. The PS wave characteristics of 4-AP and PTX models were compared. The results showed that PS wave induced by 4-AP had a wide waveform and distributed more widely. The distribution intervals were mainly distributed in the range of 100-700 ms. However, PS induced by PTX showed explosive release with higher release rate, which was mainly distributed in the range of 2 ~ 20 ms, making the sum of PS amplitudes per second significantly larger than the 4-AP model. Therefore, the PTX model of neuronal population synchronization release activities than the 4-AP model is stronger. In summary, the new spike detection algorithm can correctly identify and analyze epileptic spikes, which provides a useful data analysis tool for the study of epilepsy mechanism and evaluation of new methods of epilepsy treatment.