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

Journal of Neural Engineering

Volumn 8, Issue 3, 2011, Pages

  Lai, Hsin Yi ^a   Chen, You Yin ^a,b   Lin, Sheng Huang ^c,d   Lo, Yu Chun ^d   Tsang, Siny ^e   Chen, Shin Yuan ^c   Zhao, Wan Ting ^f   Chao, Wen Hung ^g   Chang, Yao Chuan ^a   Wu, Robby ^h   Shih, Yen Yu I ⁱ   Tsai, Sheng Tsung ^e   Jaw, Fu Shan ^c  

^a NATIONAL CHIAO TUNG UNIVERSITY   (Taiwan)

^b NATIONAL YANG MING UNIVERSITY   (Taiwan)

^c TZU CHI UNIVERSITY   (Taiwan)

^d NATIONAL TAIWAN UNIVERSITY   (Taiwan)

^e UNIVERSITY OF VIRGINIA   (United States)

^f NATIONAL TSING HUA UNIVERSITY   (Taiwan)

^g YUANPEI UNIVERSITY   (Taiwan)

^h PHILADELPHIA COLLEGE OF OSTEOPATHIC MEDICINE   (United States)

ⁱ UNIVERSITY OF TEXAS HEALTH SCIENCE CENTER AT SAN ANTONIO   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

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; 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;

EID: 79957961575     PISSN: 17412560     EISSN: 17412552     Source Type: Journal    
DOI: 10.1088/1741-2560/8/3/036003     Document Type: Article

References (54)

1. Adamos D A, Kosmidis E K and Theophilidis G 2008 Performance evaluation of PCA-based spike sorting algorithms Comput. Methods Programs Biomed. 91 232-44
2. Aharon B H, Spiro A and Stark E 2006 Spike sorting: Bayesian clustering of non-stationary data J. Neurosci. Methods 157 303-16
3. Aldenderfer M S and Blashfield R K 1984 Cluster Analysis (Beverly Hills, CA: Sage)
4. Anil K J, Robert P W D and Jianchang M 2000 Statistical pattern recognition: a review IEEE Trans. Pattern Anal. Mach. Intell. 22 4-37
5. Azzeh M, Neagu D and Cowling P 2010 Fuzzy grey relational analysis for software effort estimation J. Empirical Softw. Eng. 15 60-90
6. Bankman I, Johnson K and Schneider W 1993 Optimal detection, classification, and superposition resolution in neural waveform recordings IEEE Trans. Biomed. Eng. 40 836-41
7. Brychta R J, Tuntrakool S, Appalsamy M, Keller N R, Robertson D, Shiavi R G and Diedrich A 2007 Wavelet methods for spike detection in mouse renal sympathetic nerve activity IEEE Trans. Biomed. Eng. 54 82-93
8. Carlos V I and Donoghue J P 2007 Automated spike sorting using density grid contour clustering and subtractive waveform decomposition J. Neurosci. Methods 164 1-18
9. Chandra R and Optican L M 1997 Detection, classification, and superposition resolution of action potentials in multiunit single-channel recordings by an on-line real-time neural network IEEE Trans. Biomed. Eng. 44 403-12
10. Chang K C and Yeh M F 2005 Grey relational analysis based approach for data clustering IEE Proc.-Vis. Image Signal Process. 152 165-72
11. Chapin J K 2004 Using multi-neuron population recordings for neural prosthetics Nat. Neurosci. 7 452-5
12. D'Agostino R and Stephens M 1986 Goodness-of-Fit Techniques (New York: Dekker)
13. Daubechies I 1992 Ten Lectures on Wavelets (Philadelphia: SIAM)
14. David J K and Christopher L S 1996 The application of cluster analysis in Strategic Management Research: an analysis and critique Strateg. Manage. J. 17 441-58
15. Delescluse M and Pouzat C 2006 Efficient spike-sorting of multi-state neurons using inter-spike intervals information J. Neurosci. Methods 150 16-29
16. Deng J L 1982 Control problems of grey systems Syst. Control Lett. 1 288-94
17. Donoho D L and Johnstone J M 1994 Ideal spatial adaptation by wavelet shrinkage Biometrika 81 425-55
18. Fee M S, Mitra P P and Kleinfeld D 1996 Variability of extracellular spike waveforms of cortical neurons J. Neurophysiol. 76 3823-33
19. Geng X, Hu G and Tian X 2010 Neural spike sorting using mathematical morphology, multiwavelets transform and hierarchical clustering Neurocomputing 73 707-15
20. Goutte C, Toft P, Rostrup E, Nielsen F A and Hansen L K 1999 On clustering fMRI time series NeuroImage 9 298-310
21. Gower J C and Ross G J S 1969 Minimum spanning trees and single linkage cluster analysis J. R. Stat. Soc. C 18 54-64
22. Hariharan G and Kannan K 2010 Haar wavelet method for solving FitzHugh-Nagumo equation Int. J. Math. Stat. Sci. 2 281-5
23. Herbst J A, Gammeter S, Ferrero D and Hahnloser R H R 2008 Spike sorting with hidden Markov models J. Neurosci. Methods 174 126-34
24. Hu J, Si J, Olson B P and He J 2005 Feature detection in motor cortical spikes by principal component analysis IEEE Trans. Neural Syst. Rehabil. Eng. 13 256-62
25. Hulata E, Segev R and Eshel B J 2002 A method for spike sorting and detection based on wavelet packets and Shannon's mutual information J. Neurosci. Methods 117 1-12
26. Hulata E, Segev R, Shapira Y, Benveniste M and Ben-Jacob E 2000 Detection and sorting of neural spikes using wavelet packets Phys. Rev. Lett. 85 4637-40
27. Jain A K, Murty M N and Flynn P J 1999 Data clustering: a review ACM Comput. Surv. 31 264-323
28. Kaneko H, Suzuki S, Okada J and Akamatsu M 1999 Multineuronal spike classification based on multisite electroderecording, whole-waveform analysis, and hierarchical clustering IEEE Trans. Biomed. Eng. 46 280-90
29. Kim K H 2006 Improved algorithm for fully-automated neural spike sorting based on projection pursuit and Gaussian mixture model Int. J. Control Autom. Syst. 4 705-13
30. Kim K H and Kim S J 2000 Neural spike sorting under nearly 0-dB signal-to-noise ratio using nonlinear energy operator and artificial neural-network classifier IEEE Trans. Biomed. Eng. 47 1406-11
31. Letelier J C and Weber P P 2000 Spike sorting based on discrete wavelet transform coefficients J. Neurosci. Methods 101 93-106
32. Lewicki M S 1998 A review of methods for spike sorting: the detection and classification of neural action potentials Netw. Comput. Neural Syst. 9 53-78
33. Lin Y H, Lee P C and Chang T P 2009 Practical expert diagnosis model based on the grey relational analysis technique Expert Syst. Appl. 36 1523-8
34. Morán J, Granada E, Míguez J L and Porteiro J 2006 Use of grey relational analysis to assess and optimize small biomass boilers Fuel Process. Tech. 87 123-7
35. Murtagh F 1983 A survey of recent advances in hierarchical clustering algorithms Comput. J. 26 354-9
36. Pavlov A, Makarov V, Makarova I and Panetsos F 2007 Sorting of neural spikes: when wavelet based methods outperform principal component analysis Nat. Comput. 6 269-81
37. Pouzat C, Mazor O and Laurent G 2002 Using noise signature to optimize spike-sorting and to assess neuronal classification quality J. Neurosci. Methods 122 43-57
38. Press W H, Teukolsky S A, Vetterling W T and Flannery B P 2002 Numerical Recipes in C: the Art of Scientific Computing (New York: Cambridge University Press)
39. Quiroga R Q, Nadasdy Z and Ben-Shaul Y 2004 Unsupervised spike detection and sorting with wavelets and superparamagnetic clustering Neural Comput. 16 1661-87
40. Roh S E, Choi J H and Kim T 2008 A new action potential classifier using 3-Gaussian model fitting Neurocomputing 71 3631-4
41. Rutishauser U, Schuman E M and Mamelak A N 2006 Online detection and sorting of extracellularly recorded action potentials in human medial temporal lobe recordings, in vivo J. Neurosci. Methods 154 204-24
42. Sanguinetti G 2008 Dimensionality reduction for clustered data sets IEEE Trans. Pattern Anal. Mach. Intell. 30 535-40
43. Saul L K and Roweis S T 2003 Think globally, fit locally: unsupervised learning of low dimensional manifolds J. Mach. Learn. Res. 4 119-55
44. Shoham S, Fellows M R and Normann R A 2003 Robust, automatic spike sorting using mixtures of multivariate t-distributions J. Neurosci. Methods 127 111-22
45. Stewart C M, Newlands S D and Perachio A A 2004 Spike detection, characterization, and discrimination using feature analysis software written in LabVIEW Comput. Methods Programs Biomed. 76 239-51
46. Takekawa T, Isomura Y and Fukai T 2010 Accurate spike sorting for multi-unit recordings Eur. J. Neurosci. 31 263-72
47. Thakur P H, Lu H, Hsiao S S and Johnson K O 2007 Automated optimal detection and classification of neural action potentials in extra-cellular recordings J. Neurosci. Methods 162 364-76
48. Venkatesh S, Ayyaswamy S and Hariharan G 2010 Haar wavelet method for solving initial and boundary value problems of Bratu-type Int. J. Math. Stat. Sci. 2 64-7
49. Vollgraf R and Obermayer K 2006 Improved optimal linear filters for the discrimination of multichannel waveform templates for spike-sorting applications IEEE Signal Process. Lett. 13 121-4
50. Wheeler B C 1999 Methods for Neural Ensemble Recordings ed M Nicolelis (Boca Raton, FL: CRC Press) pp 61-76
51. Wood F, Black M J, Vargas I C, Fellows M and Donoghue J P 2004 On the variability of manual spike sorting IEEE Trans. Biomed. Eng. 51 912-8
52. Yang Z, Zhao Q and Liu W 2009 Improving spike separation using waveform derivatives J. Neural Eng. 6 046006
53. Yi D and Pin G 1992 Gray Theory and Methods (Beijing: Petroleum Industry)
54. Zouridakis G and Tam D C 2000 Identification of reliable spike templates in multi-unit extracellular recordings using fuzzy clustering Comput. Methods Programs Biomed. 61 91-8