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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...
The manual control of anaesthesia is still the dominant practice during surgery. An increasing number of studies have been conducted to explore the possibility of automating this process. The major difficulty in the design of closed-loop control during anaesthesia is the inherent patient variability due to differences in demographic and drug tolerance. These discrepancies are translated into the differences...
This paper presents a new method to monitor the depth of anaesthesia using wavelet analysis of middle latency auditory evoked potential (AEP). This method uses much less sweeps comparing to existing methods and make the fast extraction of AEP signal possible. Moreover, the result shows a better relativity with the depth of anaesthesia.
This paper introduces the development of a monitoring system to depth of anaesthesia in a surgery. We first developed the audiory evoked potential (AEP) method and the bispectrium method. Then we combined them together using a off-line trained neural networks. The preliminary results show that our calculated index is closely relative to the depth of anaesthesia.
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