The Application Study Of Lattice Complexity On Monitoring The Depth Of Anesthesia

Anesthesia affects the activity of central nervous system by using a variety of drugs to finally reach a sedation, analgesia and forgetting state which is suitable for surgical operation. During clinical anesthesia, Inadequate anesthetics may cause patients psychological trauma by intraoperative knows, Too much anesthetics will increase adverse events in patients during or after surgery. Thus Implementation accurate anesthesia by monitoring physiological data of the patients is one of the essential means to ensure the security of the anesthesia. EEG, as the macroscopic electrical activity of cerebral cortex nerve cells. Can reflect the state of the activity of the brain cortex in real time, has been used as a main method of monitoring depth of anesthesia awareness.Lattice complexity(LC) is an index contains the chaotic sequence information,and has an advantage of quantization chaos characteristic.In this paper, lattice complexity(LC) was used in EEG complexity analysis during anesthesia process, based on fine-graining method and two partition methods(permutation partition and average partition.And quantified the consciousness changes caused by anesthesia sedation.Collecting the EEG signals of 30 patients who underwent general anesthesia with propofol by clinical experiment, Following simulation calculations were conducted this paper based on this.1. we calculated the EEG Lattice Complexity and Lempel-Ziv Complexity(LZ) and compared Pearson’s correlation coefficient between LC, IZ and BIS values. And MOAA/S as an index was used to evaluate the prediction probability of LC, LZ and BIS to the sedation depth. Results showed that during anesthesia induction period, average partition with lattice complexity get better result. While during anesthesia recovery period, permutation partition with lattice complexity get better result, and the Pearson’s correlation coefficient between LC and BIS values was 0.9636. During the whole anesthesia period, Average partition with lattice complexity get better effect, and the the Pearson’s correlation coefficient between LC and BIS values was 0.8416. The fine-graining order take 3 can get good result.2. As one index can not quantify anesthesia depth accurately, three indexes LC,Spectral Edge Frequency(SEF) and Burst Suppression Ratio(BSR) were used as the input of back-propagation(BP) artificial network, BIS index was used as the output.After optimization,we find this net can used to evaluate the anesthetic depth of test data.3. LC was also used in analgesia monitoring in this paper.We calculated the LC of the power spectrum of the EEG signal which include the nociceptive stimulus (intubation), Results showed that different fine-graining indexes have different recognition ability of nociceptive stimulus. If the fine-graining index was suitable, this method can get the same result as spectral entropy. This can provide a new method for clinical analgesia monitoring.Above all, Lattice Complexity,as method of symbol sequence complexity metrics, can be used in monitoring the consciousness of anesthesia depth and analgesia monitoring.