Noise level classification for EEG using Hidden Markov Models

Haggag, Sherif, Mohamed, Shady, Bhatti, Asim, Haggag, Hussein and Nahavandi, Saeid 2015, Noise level classification for EEG using Hidden Markov Models, in SoSE 2015 : Proceedings of the 10th IEEE International Conference on System of Systems Engineering, IEEE, Piscataway, N.J., pp. 439-444, doi: 10.1109/SYSOSE.2015.7151974.

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Title Noise level classification for EEG using Hidden Markov Models
Author(s) Haggag, Sherif
Mohamed, ShadyORCID iD for Mohamed, Shady orcid.org/0000-0002-8851-1635
Bhatti, AsimORCID iD for Bhatti, Asim orcid.org/0000-0001-6876-1437
Haggag, HusseinORCID iD for Haggag, Hussein orcid.org/0000-0001-7368-6777
Nahavandi, SaeidORCID iD for Nahavandi, Saeid orcid.org/0000-0002-0360-5270
Conference name System of Systems Engineering. Conference (10th : 2015 : San Antonio, Texas)
Conference location San Antonio, Tex.
Conference dates 17-20 May. 2015
Title of proceedings SoSE 2015 : Proceedings of the 10th IEEE International Conference on System of Systems Engineering
Publication date 2015
Start page 439
End page 444
Total pages 6
Publisher IEEE
Place of publication Piscataway, N.J.
Summary EEG signal is one of the most important signals for diagnosing some diseases. EEG is always recorded with an amount of noise, the more noise is recorded the less quality is the EEG signal. The included noise can represent the quality of the recorded EEG signal, this paper proposes a signal quality assessment method for EEG signal. The method generates an automated measure to detect the noise level of the recorded EEG signal. Mel-Frequency Cepstrum Coefficient is used to represent the signals. Hidden Markov Models were used to build a classification model that classifies the EEG signals based on the noise level associated with the signal. This EEG 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. Moreover, our model was applied on an uncontrolled environment and on controlled environment and a result comparison was applied.
ISBN 9781479976119
Language eng
DOI 10.1109/SYSOSE.2015.7151974
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 ©2015, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30078833

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