北京生物医学工程2012，Vol.31Issue(3)：268-272,5.DOI:10.3969/j.issn.1002-3208.2012.03.10
基于数学形态学与核主成分分析的峰电位检测与分类方法
Unsupervised spike detection and sorting with mathematical morphology and kernel principal components analysis
摘要
Abstract
Objective We introduce a new unsupervised method for detecting and sorting spikes from extracellular recordings. Methods First, multiple mathematical morphology operation is used in signal de-noising before spike detection with a fixed threshold. Then,wavelet transform and kernel principal components analysis ( KPCA ) are performed to the detected spike waveforms to extract discriminative features. Finally, the minimum-distance clustering is proceeded to sort spikes. Results The simulation experimental results indicate that the spike detectable rate is 94% . The classification accuracy in general is over 91% and that with many superposed signals is over 88% . Conclusions The results show that the method performs quite well even with the noisy simulated spike data.关键词
峰电位/检测/分类/数学形态学/核主成分分析Key words
spike/ detection/ sorting/ mathematical morphology/ kernel principal components analysis分类
医药卫生引用本文复制引用
王冬雪,周逸峰..基于数学形态学与核主成分分析的峰电位检测与分类方法[J].北京生物医学工程,2012,31(3):268-272,5.基金项目
中央高校基本科研业务费专项资金(WK2100230002)资助 （WK2100230002）
