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Hidden Markov model neurons classification based on Mel-frequency cepstral coefficients

Haggag,S, Mohamed,S, Haggag,H and Nahavandi,S 2014, Hidden Markov model neurons classification based on Mel-frequency cepstral coefficients, in SOSE 2014 : The Socio-Technical Perspective : Proceedings of the 9th International Conference on System of Systems Engineering, Institute of Electrical and Electronics Engineers, Piscataway, N. J, pp. 166-170, doi: 10.1109/SYSOSE.2014.6892482.

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Title Hidden Markov model neurons classification based on Mel-frequency cepstral coefficients
Author(s) Haggag,S
Mohamed,SORCID iD for Mohamed,S orcid.org/0000-0002-8851-1635
Haggag,H
Nahavandi,S
Conference name System of Systems Engineering. Conference (2014 : Adelaide, South Australia)
Conference location Adelaide, South Australia
Conference dates 9-13 June 2014
Title of proceedings SOSE 2014 : The Socio-Technical Perspective : Proceedings of the 9th International Conference on System of Systems Engineering
Editor(s) [Unknown]
Publication date 2014
Conference series International Conference on System of Systems Engineering (SOSE)
Start page 166
End page 170
Total pages 5
Publisher Institute of Electrical and Electronics Engineers
Place of publication Piscataway, N. J
Keyword(s) Hidden Markov model
Kolmogorov-Smirnov test
Mel-ferquency Cepstral Coefficients
Spike Detection
Superparamagnetic clustering
Wavelets
Summary In neuroscience, the extracellular actions potentials of neurons are the most important signals, which are called spikes. However, a single extracellular electrode can capture spikes from more than one neuron. Spike sorting is an important task to diagnose various neural activities. The more we can understand neurons the more we can cure more neural diseases. The process of sorting these spikes is typically made in some steps which are detection, feature extraction and clustering. In this paper we propose to use the Mel-frequency cepstral coefficients (MFCC) to extract spike features associated with Hidden Markov model (HMM) in the clustering step. Our results show that using MFCC features can differentiate between spikes more clearly than the other feature extraction methods, and also using HMM as a clustering algorithm also yields a better sorting accuracy.
ISBN 9781479952274
Language eng
DOI 10.1109/SYSOSE.2014.6892482
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
ERA Research output type E Conference publication
Copyright notice ©2014, Institute of Electrical and Electronics Engineers
Persistent URL http://hdl.handle.net/10536/DRO/DU:30070516

Document type: Conference Paper
Collection: Centre for Intelligent Systems Research
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