Spike sorting using hidden markov models

Zhou, Hailing, Mohamed, Shady M. Korany, Bhatti, Asim, Lim, Chee Peng, Gu, Nong, Haggag, Sherif and Nahavandi, Saeid 2013, Spike sorting using hidden markov models, in ICONIP 2013 : Neural Information Processing : 20th International Conference, Daegu, Korea, November 2013 Proceedings, Part 1, Springer, Berlin, Germany, pp. 553-560, doi: 10.1007/978-3-642-42054-2_69.

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Title Spike sorting using hidden markov models
Author(s) Zhou, HailingORCID iD for Zhou, Hailing orcid.org/0000-0001-5009-4330
Mohamed, Shady M. KoranyORCID iD for Mohamed, Shady M. Korany orcid.org/0000-0002-8851-1635
Bhatti, AsimORCID iD for Bhatti, Asim orcid.org/0000-0001-6876-1437
Lim, Chee PengORCID iD for Lim, Chee Peng orcid.org/0000-0003-4191-9083
Gu, Nong
Haggag, Sherif
Nahavandi, SaeidORCID iD for Nahavandi, Saeid orcid.org/0000-0002-0360-5270
Conference name Neural Information Processing. Conference (20th : 2013 : Daegu, Korea)
Conference location Daegu, Korea
Conference dates 3-7 Nov. 2013
Title of proceedings ICONIP 2013 : Neural Information Processing : 20th International Conference, Daegu, Korea, November 2013 Proceedings, Part 1
Editor(s) Lee, Minho
Hirose, Akira
Hou, Zeng-Guang
Kil, Rhee Man
Publication date 2013
Conference series Lecture notes in computer science; v.8226
Start page 553
End page 560
Total pages 8
Publisher Springer
Place of publication Berlin, Germany
Keyword(s) Cepstrum
Confusion matrix
Spike sorting
Summary In this paper, hidden Markov models (HMM) is studied for spike sorting. We notice that HMM state sequences have capability to represent spikes precisely and concisely. We build a HMM for spikes, where HMM states respect spike significant shape variations. Four shape variations are introduced: silence, going up, going down and peak. They constitute every spike with an underlying probabilistic dependence that is modelled by HMM. Based on this representation, spikes sorting becomes a classification problem of compact HMM state sequences. In addition, we enhance the method by defining HMM on extracted Cepstrum features, which improves the accuracy of spike sorting. Simulation results demonstrate the effectiveness of the proposed method as well as the efficiency.
ISBN 3642420540
ISSN 0302-9743
Language eng
DOI 10.1007/978-3-642-42054-2_69
Field of Research 110999 Neurosciences not elsewhere classified
Socio Economic Objective 970101 Expanding Knowledge in the Mathematical Sciences
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2013, Springer
Persistent URL http://hdl.handle.net/10536/DRO/DU:30062711

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