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,T orcid.org/0000-0001-9709-1663 Khosravi,A orcid.org/0000-0001-6927-0744 Hettiarachchi,I orcid.org/0000-0002-4220-0970 Creighton,D orcid.org/0000-0002-9217-1231 Nahavandi,S orcid.org/0000-0002-0360-5270 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 Collections: Institute for Intelligent Systems Research and Innovation (IISRI) GTP Research Related Links Link Description Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Mon, 20 Apr 2015, 10:32:40 EST Mon, 20 Apr 2015, 10:34:00 EST Tue, 21 Apr 2015, 05:01:21 EST Tue, 21 Apr 2015, 06:09:00 EST Fri, 24 Apr 2015, 09:23:26 EST Fri, 24 Apr 2015, 09:29:14 EST Wed, 27 May 2015, 14:31:26 EST Fri, 09 Oct 2015, 17:08:56 EST Mon, 12 Oct 2015, 16:04:07 EST Mon, 18 Jul 2016, 15:35:41 EST Wed, 23 Nov 2016, 09:51:20 EST Wed, 23 Nov 2016, 13:28:33 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 552 Abstract Views, 2 File Downloads  -  Detailed Statistics Created: Mon, 20 Apr 2015, 10:34:00 EST

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