Monitoring the depth of anaesthesia using Hurst exponent and Bayesian methods

This study proposes a novel index ML_DoA to identify different anaesthetic states of a patient during surgery. Based on the new index ML_DoA, 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 of anaesthesia: deep anaesthesia, moderate anaesthesia, light anaesthesia and awake. Finally, the index ML_DoA is derived based on the Hurst exponent and maximum-likelihood function. The ML_DoA index is evaluated using clinically obtained EEG signals and the bispectral (BIS) data. The results show that the new index remains robust in the case of poor signal quality where BIS does not. Moreover, the new index ML_DoA responds faster than the BIS index during the anaesthetic state transitions of patients. To validate the proposed method, the analysis of variance method is used to compare the new index ML_DoA with the BIS index. The results indicate that the ML_DoA distribution is better in distinguishing the five DoA states.