多通道神经元锋电位检测和分类的新方法

大脑神经元胞外单细胞动作电位(即锋电位)的检测和分类是提取神经元脉冲序列、研究神经系统信息处理机制的关键．为了提高锋电位的检出率和分类的正确性，设计了一种处理多通道锋电位记录信号的算法，用于分析微电极阵列记录的大鼠海马神经元锋电位信号，电极阵列上的测量点排列紧密，4个通道可以同时记录到来自相同神经元的信号．该算法首先利用一种多通道阈值检测法检出四通道记录信号中的锋电位，然后利用一种基于复合锋电位的主成分特征参数分类法将锋电位分类．仿真数据和实验记录信号的检验结果表明：与相应的单通道算法相比，该算法的锋电位检出率和分类的正确性显著提高，并且可以增加单次实验测得的神经元数目．因此，该算法为实现神经元锋电位的自动检测提供了一种简单有效的新 方法．

A Novel Method for Multi-channel Neuronal Spike Detection and Classification

The detection and classification of extracellular action potentials (i.e. spike) of various single neurons from extracellular recordings are crucial for extracting neuronal spike sequences and thereby for investigating the mechanisms of neural information processing in the central nervous system. In order to increase the correctness of spike detecting and sorting, a new analysis algorithm for processing multi-channel spike signals recorded from rat hippocampi with silicon microelectrode arrays is presented. Four recording contacts on the electrode array are arranged close enough to simultaneously record spikes emitted from same neurons. Firstly, the algorithm extracts all spikes in the four channel recordings by using a multi-channel threshold detection method. Secondly, the algorithm classifies the spikes based on a principle component analysis for a specifically designed type of compound spike waveforms. The compound spike waveform is formed by linking four spike waveforms of a same neuronal firing in the four recording channels one by one in series. The test results with both synthetic datasets and experimental recordings reveal that compared with corresponding traditional single-channel algorithm, the multi-channel algorithm can significantly enhance both the number of extracted spikes and the correctness of spike classifications. The algorithm can also increase the number of isolated neurons from a single experimental preparation. These results indicate that the novel method is efficient for the automatic detection and classification of neuronal spikes.