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Sensors 2013, 13(5), 6605-6635; doi:10.3390/s130506605

Evolution of Electroencephalogram Signal Analysis Techniques during Anesthesia

1,2,* , 1
1 Department of Electrical, Electronic & Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, UKM Bangi Selangor 43600, Malaysia 2 Department of Biomedical Engineering, Al-Khwarizmi College of Engineering, Baghdad University, Baghdad 47146, Iraq
* Author to whom correspondence should be addressed.
Received: 26 March 2013 / Revised: 6 May 2013 / Accepted: 7 May 2013 / Published: 17 May 2013
(This article belongs to the Section Physical Sensors)
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Biosignal analysis is one of the most important topics that researchers have tried to develop during the last century to understand numerous human diseases. Electroencephalograms (EEGs) are one of the techniques which provides an electrical representation of biosignals that reflect changes in the activity of the human brain. Monitoring the levels of anesthesia is a very important subject, which has been proposed to avoid both patient awareness caused by inadequate dosage of anesthetic drugs and excessive use of anesthesia during surgery. This article reviews the bases of these techniques and their development within the last decades and provides a synopsis of the relevant methodologies and algorithms that are used to analyze EEG signals. In addition, it aims to present some of the physiological background of the EEG signal, developments in EEG signal processing, and the effective methods used to remove various types of noise. This review will hopefully increase efforts to develop methods that use EEG signals for determining and classifying the depth of anesthesia with a high data rate to produce a flexible and reliable detection device.
Keywords: electroencephalogram (EEG); anesthesia; detection; signal processing; features; classification electroencephalogram (EEG); anesthesia; detection; signal processing; features; classification
This is an open access article distributed under the Creative Commons Attribution License (CC BY) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Al-Kadi, M.I.; Reaz, M.B.I.; Ali, M.A.M. Evolution of Electroencephalogram Signal Analysis Techniques during Anesthesia. Sensors 2013, 13, 6605-6635.

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