Automatic spike sorting for extracellular electrophysiological recording using unsupervised single linkage clustering based on grey relational analysis

Hsin Yi Lai, You Yin Chen, Sheng Huang Lin, Yu Chun Lo, Siny Tsang, Shin Yuan Chen, Wan Ting Zhao, Wen Hung Chao, Yao Chuan Chang, Robby Wu, Yen Yu I Shih, Sheng Tsung Tsai, Fu Shan Jaw

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13 引文 斯高帕斯(Scopus)

摘要

Automatic spike sorting is a prerequisite for neuroscience research on multichannel extracellular recordings of neuronal activity. A novel spike sorting framework, combining efficient feature extraction and an unsupervised clustering method, is described here. Wavelet transform (WT) is adopted to extract features from each detected spike, and the Kolmogorov-Smirnov test (KS test) is utilized to select discriminative wavelet coefficients from the extracted features. Next, an unsupervised single linkage clustering method based on grey relational analysis (GSLC) is applied for spike clustering. The GSLC uses the grey relational grade as the similarity measure, instead of the Euclidean distance for distance calculation; the number of clusters is automatically determined by the elbow criterion in the threshold-cumulative distribution. Four simulated data sets with four noise levels and electrophysiological data recorded from the subthalamic nucleus of eight patients with Parkinson's disease during deep brain stimulation surgery are used to evaluate the performance of GSLC. Feature extraction results from the use of WT with the KS test indicate a reduced number of feature coefficients, as well as good noise rejection, despite similar spike waveforms. Accordingly, the use of GSLC for spike sorting achieves high classification accuracy in all simulated data sets. Moreover, J-measure results in the electrophysiological data indicating that the quality of spike sorting is adequate with the use of GSLC.
原文英語
文章編號036003
期刊Journal of Neural Engineering
8
發行號3
DOIs
出版狀態已發佈 - 六月 2011
對外發佈Yes

ASJC Scopus subject areas

  • Biomedical Engineering
  • Cellular and Molecular Neuroscience

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