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Noise level classification for EEG using Hidden Markov Models
conference contribution
posted on 2015-01-01, 00:00 authored by Sherif Haggag, Shady MohamedShady Mohamed, Asim BhattiAsim Bhatti, Hussein Haggag, Saeid NahavandiEEG 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.
History
Event
System of Systems Engineering. Conference (10th : 2015 : San Antonio, Texas)Pagination
439 - 444Publisher
IEEELocation
San Antonio, Tex.Place of publication
Piscataway, N.J.Publisher DOI
Start date
2015-05-17End date
2015-05-20ISBN-13
9781479976119Language
engPublication classification
E Conference publication; E1 Full written paper - refereedCopyright notice
2015, IEEETitle of proceedings
SoSE 2015 : Proceedings of the 10th IEEE International Conference on System of Systems EngineeringUsage metrics
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