Automatic spike sorting for extracellular electrophysiological recording using unsupervised single linkage clustering based on grey relational analysis
| Autor: | S. Y. Chen, Yen-Yu Ian Shih, Wen Hung Chao, Yao-Chuan Chang, Sheng Huang Lin, You Yin Chen, Robby Wu, Yu Chun Lo, Sheng Tsung Tsai, Hsin Yi Lai, Wan Ting Zhao, Siny Tsang, Fu-Shan Jaw |
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| Rok vydání: | 2011 |
| Předmět: |
Male
Computer science Speech recognition Feature extraction Single-linkage clustering Biomedical Engineering Wavelet Analysis Action Potentials Similarity measure Sensitivity and Specificity Pattern Recognition Automated Cellular and Molecular Neuroscience Humans Diagnosis Computer-Assisted Cluster analysis business.industry Wavelet transform Brain Reproducibility of Results Pattern recognition Electroencephalography Parkinson Disease Spike sorting Feature (computer vision) Spike (software development) Female Artificial intelligence business Algorithms |
| Zdroj: | Journal of neural engineering. 8(3) |
| ISSN: | 1741-2552 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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