中文摘要：

为了考察深部脑刺激的高频电刺激（HFS）期间各种神经元单体的动作电位发放活动,排除电刺激诱发的群峰电位的干扰,设计了一种窗口检测新算法,直接用于检测宽频带记录信号中的锋电位。并且,利用仿真数据和大鼠海马CA1区实验记录数据验证此算法的有效性。结果表明,新算法的锋电位检出率显著大于常规阈值法,而误检率则显著小于阈值法。该算法对于高频刺激期间的仿真数据的锋电位检出率可达95%,误检率则仅为4%;对于7只大鼠的顺向高频刺激实验记录数据的平均锋电位检出率为88±1.4%,而误检率为4.6±1.1%。总之,新窗口法可以正确检测高频刺激期间的锋电位,用于研究各种神经元单体在电刺激期间的不同响应活动,为深入揭示深部脑刺激的神经网络机制提供了有用的新工具。

英文摘要：

To investigate the activity of various single neurons during the periods of high frequency stimulation（ HFS） of deep brain stimulation（ DBS） and eliminate the interferences of the population spikes evoked by the stimulation,a new algorithm based on window detection was designed and used to detect the spikes in broadband-frequency recording signals directly. Both simulation data and the experiment data recorded from the rat hippocampal CA1 region were used to verify the effectiveness of the algorithm. The results show that the new algorithm has significantly higher detection ratio and lower false positive ratio than conventional threshold method. For the simulation data during the HFS period,the spike detection ratio of the new algorithm is up to 95%,and the false positive ratio is only 4%.For the experiment data from 7 rats under orthodromic-HFS,the average spike detection ratio of the algorithm is 88 ± 1. 4% and the false positive ratio is 4. 6 ± 1. 1%. In conclusion,the new window detection algorithm can correctly detect the spikes during the periods of HFS,and can be used to study the response activity of various neurons during electrical stimulation period,which provides a useful new tool for further revealing the neuronal network mechanisms of DBS.