Neural spike sorting using mathematical morphology, multiwavelets transform and hierarchical clustering
Neural spike sorting using mathematical morphology, multiwavelets transform and hierarchical clustering
复制标题
使用数学形态学、多小波变换和层次聚类进行神经尖峰排序
DOI：
10.1016/j.neucom.2008.11.034
复制
发表时间：
2010-01-01
期刊：
影响因子：
6
通讯作者：
Tian, Xin
中科院分区：
文献类型：
--
作者：
Geng, Xinling;Hu, Guangshu;Tian, Xin
神经尖峰分类是多尖峰序列数据分析不可缺少的第一步。由于噪声降低了大多数现有的尖峰排序方法的性能，因此本文提出了一种新的尖峰排序算法框架，该算法对重噪声的影响较小。首先，使用数学形态学运算来简化尖峰事件的检测过程，特别是在强噪声情况下。然后对检测到的尖峰波形进行多小波变换提取判别特征；最后，使用异常值去除过程进行分层聚类，分离出前10个可区分的多小波系数。结果表明，该方法即使对噪声较大的模拟尖峰数据也有很好的处理效果。(C) 2009 Elsevier B.V.版权所有
Neural spike sorting is an indispensable first step for the analysis of multiple spike train data. As it is very common that noises degrade the performance of most of the available spike sorting method, in this paper we have proposed a novel spike sorting algorithm framework which seems less susceptible to heavy noises. At first, mathematical morphology operation is used to facilitate the spike event detection process, especially in strong noisy situations. Then, multiwavelets transform is performed to the detected spike waveforms to extract discriminative features. Finally, hierarchical clustering with an outlier removal process proceeds to separate the first 10 distinguishable multiwavelets coefficients. The results show that our spike sorting method performs quite well even for the heavy noisy simulated spike data. (C) 2009 Elsevier B.V. All rights reserved.