Classification Technique of Neuronal Spikes Based on Singular Spectrum Entropy
Yun Li · Hangtian yixue yu yixue gongcheng · 2011
Objective To realize the accurate detection and classification of neuronal spikes,as well as provide the premise for further analyzing and decoding neural signals.Methods An improved threshold method was adopted to find out effective spikes of neuronal action potentials from the noisy electroneurographic signals,which were collected with the implanted multi-electrode array.Then based on the singular value decomposition(SVD),the features of original spikes were characterized with singular spectrum entropy(SSE).Kolmogorov-Smirnov test was used to reduce the dimensions of features.A two-dimensional feature vector was selected to gain the best result of clustering through interactive mode.Finally,the classification of neuronal spikes was achieved with C-means clustering algorithm.Results The perfect results of clustering were gained with this feature of SSE from many groups of simulated or real electroneurographic signals.The accuracy of simulated signal classification was almost above 98%.Conclusion The features based on SSE of spikes can be perfectly applied to distinguishing their dynamic characteristics and be the effective basis of classification of neuronal spikes.