Removing noise from electroencephalogram signals for BIS based depth of anaesthesia monitors
Masters Thesis
Title | Removing noise from electroencephalogram signals for |
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Type | Masters Thesis |
Authors | |
Author | Palendeng, Mario Elvis |
Supervisor | Wen, Peng (Paul) |
Goh, Steven | |
Institution of Origin | University of Southern Queensland |
Qualification Name | Master of Engineering (Research) |
Number of Pages | 133 |
Year | 2011 |
Abstract | The assessment of patient has changed from the physical assessment to digital assessment. One significant example is the assessment of the depth of anaesthesia (DoA). It has changed from physical to digital assessment using DoA monitor. DoA monitor uses the electroencephalogram (EEG) signal as its input. The processes include the digitising, filtering and signal analysing. This study focuses on filtering process to reduce noise in the EEG signal. Noises in EEG signals could affect the accuracy of DoA monitor. The noises in EEG signal are from the muscle, eye movement and blinking, power line, and interference There are three methods which are introduced to remove noise from EEG signals. The first technique is adaptive least mean square technique, which is able to find the second method is Wavelet transform. In this technique, EEG signal is decomposed into five levels using the Stationary Wavelet Transform (SWT). The first step of this filtering is to eliminate high frequency noise in the EEG signal. The The third method is a combination of adaptive LMS and wavelet transforms method. The result from this study shows that the wavelet transform adaptive filter is able to |
Keywords | EEG signals; DoA monitors; noise reduction |
ANZSRC Field of Research 2020 | 320201. Anaesthesiology |
Byline Affiliations | Department of Electrical, Electronic and Computer Engineering |
https://research.usq.edu.au/item/q1yyq/removing-noise-from-electroencephalogram-signals-for-bis-based-depth-of-anaesthesia-monitors
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