You are not logged in.

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.

Attached Files
Name Description MIMEType Size Downloads

Title Classification of neural action potentials using mean shift clustering
Author(s) Nguyen,TORCID iD for Nguyen,T orcid.org/0000-0001-9709-1663
Khosravi,AORCID iD for Khosravi,A orcid.org/0000-0001-6927-0744
Hettiarachchi,IORCID iD for Hettiarachchi,I orcid.org/0000-0002-4220-0970
Creighton,D
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
Persistent URL http://hdl.handle.net/10536/DRO/DU:30072470

Document type: Conference Paper
Collection: Centre for Intelligent Systems Research
Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 0 times in TR Web of Science
Scopus Citation Count Cited 0 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 180 Abstract Views, 1 File Downloads  -  Detailed Statistics
Created: Mon, 20 Apr 2015, 10:34:00 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.