Automatic Spike Sorting for Extracellular Electrophysiological Recording Using Unsupervised Single Linkage Clustering Based on Grey Relational Analysis
Resource
JOURNAL OF NEURAL ENGINEERING v.8 n.3 pp.AR 036003
Journal
Journal of Neural Engineering
Pages
36003
Date Issued
2011
Date
2011
Author(s)
LAI, HSIN-YI
CHEN, YOU-YIN
LIN, SHENG-HUANG
LO, YU-CHUN
CHEN, SHIN-YUAN
CHAO, WEN-HUNG
SHIH, YEN-YU I.
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 Parkinsons 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.
SDGs
Other Subjects
Classification accuracy; Deep brain stimulation; Distance calculation; Electrophysiological recordings; Euclidean distance; Extracellular; Extracellular recording; Grey relational analysis; Grey relational grade; K-S test; Kolmogorov-Smirnov test; Multi-channel; Neuronal activities; Noise levels; Noise rejection; Number of clusters; Parkinson's disease; Similarity measure; Simulated datasets; Single linkage clustering; Spike waveforms; Spike-sorting; Subthalamic nucleus; Unsupervised clustering methods; Wavelet coefficients; Classification (of information); Electrophysiology; Face recognition; Feature extraction; Information theory; Sorting; Surgery; Wavelet transforms; Cluster analysis; adult; analytic method; article; automatic spike sorting; automation; brain depth stimulation; cellular parameters; conceptual framework; extracellular recording; female; grey relational analysis; human; Kolmogorov Smirnov test; male; nervous system electrophysiology; nervous system parameters; noise; normal distribution; Parkinson disease; Poisson distribution; principal component analysis; priority journal; statistical analysis; subthalamic nucleus; unsupervised single clustering method; action potential; algorithm; automated pattern recognition; brain; computer assisted diagnosis; electroencephalography; methodology; Parkinson disease; pathophysiology; reproducibility; sensitivity and specificity; wavelet analysis; Action Potentials; Algorithms; Brain; Diagnosis, Computer-Assisted; Electroencephalography; Female; Humans; Male; Parkinson Disease; Pattern Recognition, Automated; Reproducibility of Results; Sensitivity and Specificity; Wavelet Analysis