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This study proposes a novel index MLDoA to identify different anaesthetic states of a patient during surgery. Based on the new index MLDoA, the assessment of depth of anaesthesia (DoA) for a patient can be clearly monitored. Firstly, a modified Bayesian wavelet threshold is proposed to de-noise the electroencephalogram (EEG) signals. Secondly, the Hurst exponent is obtained to classify four states...
This paper is a comprehensive literature review on the Depth of Anaesthesia (DoA) monitoring problem. We first investigate the current clinical practice, then briefly introduce the DoA monitors, finally we analyse and discuss the reliability and accuracy of current DoA assessment practice. In this study we find that most of the responses suppressed by anaestheic agents are not of the central nervous...
Electroencephalogram (EEG) signals are often contaminated with artifacts such as electromyography (EMG), eye blink and eye ball movement. These contaminated EEG signals may give incorrect values of Bispectral Index. If fixed band-pass filter is used to filter the overlapping signals between the EEG and the artifacts, the useful information in EEG signal could be lost. This paper proposes a method...
In monitoring the depth of anaesthesia, raw EEG signals are recorded by means of an adhesive sensor attached to the forehead. The raw EEG signal is often corrupted by spike, low frequency and high frequency noise. Removal of such noise improves clinical utility and this paper presents a novel method which uses a double wavelet-based de-noising algorithm. The results of experimental simulations show...
In this study, two methods. Auditory evoked potential (AEP) and bispectrum, based on EEG signal processing are employed in monitoring depth of anaesthesia. Auditory evoked potential is obtained using wavelet analysis in order to achieve a fast extraction. Both AEP and bispectrum methods show good relativity with the depth of anaesthesia. Besides, both methods are integrated in a software developed...
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