Predicting sensitivity to general anesthesia: Bispectral index versus Checkpoint-Decomposition Algorithm

Despite a large effort in EEG signal processing, classification algorithms, deep-learning approach, predicting the sensitivity to general anesthesia (GA) remains a daunting hurdle. We compare here the ability of the Bispectral Index (BIS), developed more that twenty years ago to monitor the depth of anesthesia, with the real-time checkpoint-decomposition algorithm (CDA) to evaluate the patient sensitivity from the early induction phase of GA. Using EEG recorded in children anesthetised with propofol, we computed three parameters extracted from the BIS: 1-the minimum value (nadir) of the BIS, 2-the time to reach the minimum and 3-the duration spent below 40 during the first 10 minutes. Using a logistic regression procedure, we report that these parameters provide a poor prediction of sensitivity compared to the CDA, that combined the first occurrence time of iso-electric EEG traces, fraction of suppressions of the -band and its first occurrence time. Finally, we correlate the BIS values with the maximum power frequency of the -band, the proportion of -suppressions (S) and iso-electric suppressions (IES) as well as the and{delta} power ratios. To conclude, the checkpoint-decomposition algorithm complements the EEG indices such as the BIS to anticipate the sensitivity to GA.