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), , Springer, Berlin, Germany, pp.501-508, doi: 10.1007/978-3-319-12643-2_61.

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Title Neuron’s spikes noise level classification using hidden markov models
Author(s) Haggag,S
Mohamed,SORCID iD for Mohamed,S
Bhatti,AORCID iD for Bhatti,A
Haggag,HORCID iD for Haggag,H
Nahavandi,SORCID iD for Nahavandi,S
Editor(s) Loo,CK
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
Computer Science, Artificial Intelligence
Computer Science, Information Systems
Computer Science, Theory & Methods
Computer Science
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
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
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