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Results
A total of 25 men and 35 women were included, with a total time of procedure average of 109.62 min. Regarding the EEG analysis and CBI, the results show a high Pearson correlation between the CBI and the indices of the entropy module. Nevertheless, a high Pearson correlation does not necessarily imply that the behaviour of the indices agrees. On other hand, lower correlation values were reported by the intraclass correlation coefficient between CBI and the entropy module indices. In figure 1, the probability of prediction and the box diagrams corresponding to the patterns defined in the EEG. Li (light anaesthesia in recovery) and Lr (light anaesthesia on induction) were grouped in the same anaesthetic class or category, also Ak (awakened) and Rc (awakened, recovery). A higher prediction probability was provided by the CBI (Pk=0.935), SE (Pk=0.884) and RE (Pk=0.899).
Box plot diagrams for EEG patterns associated with previously defined clinical states, and prediction probability values associated with CBI, SE and RE. Ak, awakened; Bs, deep anaesthesia associated with suppression burst pattern; CBI, Complexity Brainwave Index; Da, deep anaesthesia; Ga, general anaesthesia; La, light dose; Li, light anaesthesia on induction; Lr, light anaesthesia in recovery; Pmk, probability of paired prediction; Rc, awakened, recovery; RE, response entropy; SE, state entropy.
A greater dispersion is observed in the SE and RE indices, a partial overlap can also be seen in the boxes associated with deep anaesthesia and general anaesthesia in these indices. A high Pearson correlation might be explained by the coinciding values corresponding to the awake and general anaesthesia states. The Bland-Altman graph of figure 2 shows that the differences between CBI and the entropy module indices exceed the concordance limits mainly for average values between 60 and 80 and 20 and 40, respectively. This suggests a lack of concordance in the states of light anaesthesia (estimated range: 60–80) and deep anaesthesia (estimated range: 20–40). The CBI, SE and RE associated with the defined clinical events are presented in figure 3.
Bland-Altman graphs to evaluate the agreement between CBI and the SE and RE indices. CBI, Complexity Brainwave Index; ICC, intraclass correlation coefficient; RE, response entropy; SE, state entropy. *The limits of agreement are defined as the average value (red line segmented mean)±2 SD (red line segmented upper and lower).
Values of CBI, SE and RE to different states clinical. CBI, Complexity Brainwave Index; RE, response entropy; SE, state entropy. *Triangle pointing down: induction of total intravenous anesthesia; circle: beginning of airway management; diamond: beginning of surgery; square: end of surgery; triangle pointing up: start of extubation. Figure developed by the author.
In the present article, we review the probability of prediction of the patient’s condition was estimated for all predictors shown in figure 4. In table 1 La (light dose), the CBI showed a similar performance when compared with the other indices being; SD1—light dose the best with a Pmk of 0.86, followed by CSI—light dose with Pmk of 0.85, CVI—the 0.84 Pmk and CBI 0.83.
Box plot diagram for probability of prediction (Pk) of the patient’s condition for central nervous system and autonomic nervous system indices. Ak, awakened; Bs, deep anaesthesia associated with suppression burst pattern; CBI, Complexity Brainwave Index; CSI, Cardiac Sympathetic Index; CVI, Cardiac Vagal Index; Da, deep anaesthesia; Ga, general anaesthesia; La, light dose; Li, light anaesthesia on induction; Lr, light anaesthesia in recovery; Rc, awakened, recovery; SD1/SD2, Poincare chart descriptors; WC-HF, high frequency component; WC-LF, low frequency component; WC-HFn, high frequency power of wavelet coefficients, and respective normalisation; WC-LFn, low frequency power of wavelet coefficients, and respective normalisation. *A total of 25 light analgesia states were identified—La. There is a reduction in the performance of CBI (from 0.935 to 0.823) when considering the event light dose—La, this mainly due to overlap with the range of values associated with the event of general anaesthesia—Ga. It can be noted that SNA-related indices alone provide a poor probability of predicting the anaesthetic depth (around 0.5, which indicates that the prediction isn’t better than chance). However, the moustache diagrams seem to indicate differences in respect to other states in the methods derived from the analysis of the Poincare chart.
Probability of paired prediction
The capacity and clinical skills of trained medical staff may be affected by external factors such as personal problems, work fatigue, among others. Besides, a physician’s learning curve is not a constant independent of the previously mentioned factors, that’s why it’s necessary to compare the most promising machine learning methods to classify different anaesthetic levels obtaining the best outcome. In this study, the following results were obtained: In the decision tree, data set classification error and cross validation error were lowest with the data sets combinations of CBI–CVI–NIBP and CBI–CSI–NIBP. In the Bagging and adaptive Boosting Assembly methods, the CBI–CSI–NIBP and CBI–CVI–CSI data set groups showed the lowest classification error and X-Val errors. In the case of the neuronal network, lowest classification error and X-Val values were in the CBI–CVI–NIBP group. On the neuro-adaptive fuzzy inference system method, the CBI-CVI data set presented the lowest errors. However, when comparing all the previously mentioned methods, the neuronal network method showed the lowest classification error and X-Val values with the CBI–CVI–NIBP (table 2).
Classifiers performance in deep anaesthesia
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