神经细胞群峰电位的自动定量分析

Automatic quantitative analysis methods for neuronal population spikes

为了实现具有多个波峰的群峰电位(population spike,PS)幅值的自动分析,设计了一种新的差分算法和海岸线算法,应用于高钾低钙环境下海马神经细胞的诱发群峰电位的计算,并与常规极值搜索法进行比较.结果显示,差分算法的结果与标准值拟合最好,检出的波峰个数也接近人工测量值.海岸线算法的结果与标准值也有良好的相关性.总之,新的差分算法具有精度高、特征点定位准确的优点,可以克服常规极值搜索法的一些不足之处.海岸线算法具有良好的精度,运算速度快,尤其适用于实时检测.

A novel differential algorithm and a coastline burst index （CBI） method were designed to automatically analyze the amplitudes of population spikes （PS）. By using these two methods, the neuronal evoked response potentials in the hippocampus under high-potassium low-calcium condition were calculated and compared with those of the traditional extremum searching algorithm. Results showed that among these methods, the data of differential algorithm fitted the standard values best. The spike numbers detected by using the differential algorithm agreed with the manual results very well. The data of CBI method also correlated with the standard values nicely. Taken together, these data indicate that the differential method is more precise than the traditional extremum searching algorithm in extracting key points of the recoding potentials, therefore the method is good for automatically analyzing population spike waves under varied neuronal excitatory condition. The CBI method has a proper precise and a high calculation speed, so it is especially suitable for on-line calculations.