Title :
A comparison of methods for clustering electrophysiological multineuron recordings
Author :
Sim, A.W.K. ; Jin, C.T. ; Chan, L.W. ; Leong, P.H.W.
Author_Institution :
Dept. of Comput. Sci. & Eng., Chinese Univ. of Hong Kong, Shatin, Hong Kong
fDate :
29 Oct-1 Nov 1998
Abstract :
Techniques for the automatic clustering of extracellular multineuron recordings from the nervous system are compared for efficiency and accuracy. Selected waveforms were combined with noise to form test data with known classifications. After identical preprocessing using a Schmitt trigger threshold detector, the K-means, template matching and ART2 algorithms were applied to the same data. Measurements of the efficiency and utility of the three algorithms are presented using both the raw waveforms and the weightings of the first two principal components. Additionally, all three algorithms were tested with data obtained from electrophysiological experiments
Keywords :
ART neural nets; bioelectric potentials; electroencephalography; medical signal processing; neurophysiology; pattern clustering; signal classification; unsupervised learning; ART2 algorithms; K-means algorithms; Schmitt trigger threshold detector; accuracy; automatic clustering; clustering methods comparison; competitive learning; computer spike discrimination; covariance matrix; efficiency; electrophysiological multineuron recordings; extracellular multineuron recordings; first two principal components; multi-unit spike trains; nervous system; neural network; template matching algorithms; Artificial neural networks; Clustering algorithms; Electrodes; Extracellular; Hardware; Neurons; Shape; Signal processing algorithms; Sorting; Testing;
Conference_Titel :
Engineering in Medicine and Biology Society, 1998. Proceedings of the 20th Annual International Conference of the IEEE
Conference_Location :
Hong Kong
Print_ISBN :
0-7803-5164-9
DOI :
10.1109/IEMBS.1998.747138
