ABSTRACT
This paper proposes an anaesthesia monitoring system that accurately measures the depth of anaesthesia through 40-Hz auditory steady-state response. With accurate and fast depth of anaesthesia measuring, the monitor can reduce the incidence of awareness during surgical operation. The proposed denoising method for extracting 40-Hz auditory steady-state cycles, adaptive multilevel wavelet denoising, enabled the system to extract auditory steady-state response cycles from fewer epochs and over short periods of time which is of crucial importance in monitoring anaesthesia. The noise estimation scheme, adaptive threshold levels, rearranging, and multilevel denoising of frames increase the accuracy and signal to noise ratio of the extracted cycles. The modified fuzzy c-means clustering scheme, proposed to improve clustering performance in noisy data bases where no prior information about the level of noise and signal energy is available, is used for clustering the auditory steady-state cycles. Weighting the features with a novel algorithm and based on their differentiating role in clustering, the modified fuzzy c-means improves fuzziness in cluster partitions and the geometrical structure of the data. An index called depth of anaesthesia index is defined and determined at each cycle based on the clustering information of the cycle and the previous ones. The algorithm is applied to auditory steady-state response signals recorded from 20 human subjects during surgical operations with Propofol-induced general anaesthesia. The accuracy of the depth of anaesthesia index is validated through the subjects' medical markers, clinical parameters, and the recorded bispectral index during the induction phase. Depth of anaesthesia index is verified to be accurate and able to detect fast transitions between different levels of anaesthesia. The computed depth of anaesthesia indices detected the induction of anaesthesia on average 55 s faster than bispectral index and 17 s earlier than loss of eyelash reflex.
