北京生物医学工程

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

作者：王冬雪周逸峰

单位：中国科学技术大学电子科学技术系（合肥 200027）

分类号：

出版年·卷·期（页码）：2012·31·3（268-272）

摘要：

目的为抑制高强度背景噪声及信号叠加的干扰，提高峰电位的检出率和分类的正确性，本文提出一种新的无监督方法。方法首先，应用数学形态学的复合操作对信号进行降噪，采用定阈值提取峰电位。然后，小波变换和核主成分分析法(kernel principal components analysis,KPCA)相结合，对已提取的峰电位波形进行特征提取。最后，用改进的最小距离法实现峰电位分类。结果仿真实验结果表明，此方法对于不同噪声强度的信号，峰电位检出率达94%，总分类正确率91%以上，其中大量叠加信号的分类正确率88%以上。结论本方法能在有效抑制噪声的基础上，准确提取峰电位并有效分类。

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.

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