title: Automatic spike sorting for extracellular electrophysiological recording using unsupervised single linkage clustering based on grey relational analysis.
abstract: 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.
authors.affiliation: Department of Electrical Engineering, National Chiao Tung University, No 1001, Ta-Hsueh Rd, Hsinchu, Taiwan 300, Republic of China.
authors.forename: Hsin-Yi
authors.initials: HY
authors.lastname: Lai
authors.forename: You-Yin
authors.initials: YY
authors.lastname: Chen
authors.forename: Sheng-Huang
authors.initials: SH
authors.lastname: Lin
authors.forename: Yu-Chun
authors.initials: YC
authors.lastname: Lo
authors.forename: Siny
authors.initials: S
authors.lastname: Tsang
authors.forename: Shin-Yuan
authors.initials: SY
authors.lastname: Chen
authors.forename: Wan-Ting
authors.initials: WT
authors.lastname: Zhao
authors.forename: Wen-Hung
authors.initials: WH
authors.lastname: Chao
authors.forename: Yao-Chuan
authors.initials: YC
authors.lastname: Chang
authors.forename: Robby
authors.initials: R
authors.lastname: Wu
authors.forename: Yen-Yu I
authors.initials: YY
authors.lastname: Shih
authors.forename: Sheng-Tsung
authors.initials: ST
authors.lastname: Tsai
authors.forename: Fu-Shan
authors.initials: FS
authors.lastname: Jaw
mesh_terms: D000200:Action Potentials; D000465:Algorithms; D001921:Brain / Q000503:physiopathology*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004569:Electroencephalography / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D010300:Parkinson Disease / Q000175:diagnosis* / Q000503:physiopathology*; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D058067:Wavelet Analysis