You are not logged in.

Neuron’s spikes noise level classification using hidden markov models

Haggag,S, Mohamed,S, Bhatti,A, Haggag,H and Nahavandi,S 2014, Neuron’s spikes noise level classification using hidden markov models. In Loo,CK, Yap,KS, Wong,KW, Teoh,A and Huang,K (ed), Neural information processing : 21st International Conference ICONIP 2014 Kuching, Malaysia, November 3-6, 2014 Proceedings, Part III, Springer, Berlin, Germany, pp.501-508, doi: 10.1007/978-3-319-12643-2_61.

Attached Files
Name Description MIMEType Size Downloads

Title Neuron’s spikes noise level classification using hidden markov models
Author(s) Haggag,S
Mohamed,SORCID iD for Mohamed,S orcid.org/0000-0002-8851-1635
Bhatti,AORCID iD for Bhatti,A orcid.org/0000-0001-6876-1437
Haggag,H
Nahavandi,S
Title of book Neural information processing : 21st International Conference ICONIP 2014 Kuching, Malaysia, November 3-6, 2014 Proceedings, Part III
Editor(s) Loo,CK
Yap,KS
Wong,KW
Teoh,A
Huang,K
Publication date 2014
Series Lecture notes in computer science ; v.8836
Chapter number 61
Total chapters 83
Start page 501
End page 508
Total pages 8
Publisher Springer
Place of Publication Berlin, Germany
Keyword(s) Hidden Markov Model
Mel-Frequency Cepstrum Coefficient
Multichannel systems
Neural signal
Science & Technology
Technology
Computer Science, Artificial Intelligence
Computer Science, Information Systems
Computer Science, Theory & Methods
Computer Science
SYSTEM
Summary Considering that the uncertainty noise produced the decline in the quality of collected neural signal, this paper proposes a signal quality assessment method for neural signal. The method makes an automated measure to detect the noise levels in neural signal. Hidden Markov Models were used to build a classification model that classifies the neural spikes based on the noise level associated with the signal. This neural quality assessment measure will help doctors and researchers to focus on the patterns in the signal that have high signal to noise ratio and carry more information.
ISBN 9783319126425
ISSN 0302-9743
1611-3349
Language eng
DOI 10.1007/978-3-319-12643-2_61
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
Socio Economic Objective 920111 Nervous System and Disorders
HERDC Research category B1 Book chapter
ERA Research output type B Book chapter
Copyright notice ©2014, Springer
Persistent URL http://hdl.handle.net/10536/DRO/DU:30071097

Document type: Book Chapter
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 3 times in TR Web of Science
Scopus Citation Count Cited 3 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 108 Abstract Views, 2 File Downloads  -  Detailed Statistics
Created: Fri, 20 Mar 2015, 14:27:06 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.