Complex networks approach for depth of anesthesia assessment

Despite numerous attempts to develop a reliable depth of anesthesia (DoA) index to avoid patients' intraoperative awareness during surgery, designing an accurate DoA index is a grand challenge in anesthesia research. In this paper, an attempt is made to design a new DoA index. We applied a statistical model and spectral graph wavelet transform (SGWT) to monitor the DoA. The de-noised electroencephalography (EEG) signals are partitioned into segments using a window technique. The window size is determined empirically, then each EEG segment is divided into sub-blocks to make the signal quasi stationary. 10 statistical characteristics are extracted from each sub-block. As a result, a vector of statistical characteristics is pulled out from each segment. Each vector of the features is then mapped as a weighted graph and spectral graph wavelet transform is performed. The total energy of wavelet coefficients at different scales is tested. The energy of wavelet coefficients at scale 3 is selected to form a SGWTDoA function. The SGWTDoA is evaluated using an anesthesia EEG recordings and the bispectral (BIS) from 22 subjects. The Bland-Altman, regression, Q-Q plot and Pearson correlation are used to verify the agreement between the SGWTDoA and the BIS. The experimental results demonstrate that the SGWTDoA has the ability to estimate the DoA accurately. The SGWTDoA is also compared and tested with the BIS in the case of poor signal quality. Our findings show that, the SGWTDoA can reflect the transition from unconsciousness to consciousness efficiently even for a poor signal while the BIS fails to display the DoA values on the monitor.