Fuzzy logic model to describe anesthetic effect and muscular influence on EEG Cerebral State Index

An automatic classifier of emotions built from entropy of noise

The electrocardiogram (ECG) signal has been widely used to study the physiological substrates of emotion. However, searching for better filtering techniques in order to obtain a signal with better quality and with the maximum relevant information remains an important issue for researchers in this field. Signal processing is largely performed for ECG analysis and interpretation, but this process can be susceptible to error in the delineation phase. In addition, it can lead to the loss of important information that is usually considered as noise and, consequently, discarded from the analysis. The goal of this study was to evaluate if the ECG noise allows for the classification of emotions, while using its entropy as an input in a decision tree classifier. We collected the ECG signal from 25 healthy participants while they were presented with videos eliciting negative (fear and disgust) and neutral emotions. The results indicated that the neutral condition showed a perfect identification (100%), whereas the classification of negative emotions indicated good identification performances (60% of sensitivity and 80% of specificity). These results suggest that the entropy of noise contains relevant information that can be useful to improve the analysis of the physiological correlates of emotion.

To clarify features of photoplethysmography in monitoring balanced anesthesia, compared with Cerebral State Index

Background

Although photoplethysmography and cerebral state index (CSI) have been used as indices in monitoring vital signs perioperatively, there are only a few reports comparing the performance of photoplethysmography with CSI in monitoring anaesthesia depth. The aim of the present study was to clarify features of photoplethysmography in monitoring balanced general anesthesia compared with CSI.

Material/Methods

Forty-five patients undergoing elective operation under general anaesthesia were enrolled in this study. Anaesthesia was induced with target-controlled infusion propofol. The photoplethysmogram, CSI, Modified Observer’s Assessment of Alertness/Sedation Scale (MOAAS), and mean arterial pressure (MAP) were continuously monitored and recorded. Finger photoplethysmogram amplitude (PPGA) and pulse beat interval (PBI) were calculated off-line.

Results

For the period of time from pre-induction to pre-intubation, the coefficient of correlation between MOAAS and CSI was higher than those between MOAAS and PPGA, PBI, and MAP. CSI showed higher prediction probabilities (P_k) to differentiate the levels of MOAAS than did PPGA, PBI, and MAP. PPGA, PBI, and MAP values showed significant differences between before and after intubation, as well as pre- and post-incision (P<0.05), but no significant changes in cerebral state index (P>0.05).

Conclusions

The present study shows that photoplethysmography-derived parameters appear to be more suitable in monitoring the nociceptive component of balanced general anesthesia, while CSI performs well in detecting the sedation or hypnotic component of balanced general anesthesia.