Classification of neural action potentials using mean shift clustering

Nguyen,T, Khosravi,A, Hettiarachchi,I, Creighton,D and Nahavandi,S 2014, Classification of neural action potentials using mean shift clustering, in SMC 2014 : Proceedings of the 2014 IEEE International Conference on Systems, Man, and Cybernetics, IEEE, Piscataway, NJ, pp. 1247-1252, doi: 10.1109/SMC.2014.6974085.

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Title Classification of neural action potentials using mean shift clustering
Author(s) Nguyen,TORCID iD for Nguyen,T
Khosravi,AORCID iD for Khosravi,A
Hettiarachchi,IORCID iD for Hettiarachchi,I
Creighton,DORCID iD for Creighton,D
Nahavandi,SORCID iD for Nahavandi,S
Conference name Systems, Man, and Cybernetics. Conference (2014 : San Diego, California)
Conference location San Diego, California
Conference dates 2014/10/5 - 2014/10/8
Title of proceedings SMC 2014 : Proceedings of the 2014 IEEE International Conference on Systems, Man, and Cybernetics
Editor(s) [Unknown]
Publication date 2014
Conference series Systems, Man, and Cybernetics Conference
Start page 1247
End page 1252
Total pages 6
Publisher IEEE
Place of publication Piscataway, NJ
Summary  Understanding neural functions requires the observation of the activities of single neurons that are represented via electrophysiological data. Processing and understanding these data are challenging problems in biomedical engineering. A microelectrode commonly records the activity of multiple neurons. Spike sorting is a process of classifying every single action potential (spike) to a particular neuron. This paper proposes a combination between diffusion maps (DM) and mean shift clustering method for spike sorting. DM is utilized to extract spike features, which are highly capable of discriminating different spike shapes. Mean shift clustering provides an automatic unsupervised clustering, which takes extracted features from DM as inputs. Experimental results show a noticeable dominance of the features extracted by DM compared to those selected by wavelet transformation (WT). Accordingly, the proposed integrated method is significantly superior to the popular existing combination of WT and superparamagnetic clustering regarding spike sorting accuracy.
ISBN 9781479938391
Language eng
DOI 10.1109/SMC.2014.6974085
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2014, IEEE
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