Anesthesia State Monitoring Method Based On Local Field Potential In Human Hippocampus

Clinically,EEG is an important tool for the study of brain science and an indicator for evaluating the functional state of the brain.It is widely used in the diagnosis and evaluation of neurological diseases and psychiatric diseases.Nowadays,with the rapid development of molecular biology and neuroimaging technologies,many new technologies have emerged,which have promoted clinical diagnosis and research in the field of neuroscience.It still has its own unique advantages.General anesthesia is a mode of temporary depression of the central nervous system by anesthetic drugs.The clinical manifestations are loss of systemic pain sensation,memory loss,muscle relaxation,and the basic neural mechanism is still unclear.Accurate anesthesia monitoring during surgery is essential.During the operation,the patient’s anesthesia state is judged by monitoring the patient’s EEG signals.In this paper,6 patients with refractory epilepsy are taken as the research objects,and an anesthesia state monitoring method based on local field potential signals in the hippocampus is proposed.The main work includes:1.According to the characteristics of nerve signals in anesthesia,the characteristics of local field potential signals in the hippocampus of anesthesia were studied from different angles,and the characteristics related to anesthesia were found,and the wavelet entropy and permutation entropy features were extracted.Anesthesia state is estimated,and an anesthesia state discrimination method based on the local field potential signal in the hippocampus is proposed,which lays the foundation for further realization of anesthesia depth estimation and establishment of anesthesia state analysis and identification system,and for doctors to judge the anesthesia state of patients during surgery.Provide evidence.The experimental results show that the feature set of this method is effective in the classification of awake and unconscious states of EEG in the hippocampus.2.The feature based on information entropy is fused with the sequence features extracted by the bidirectional long-term memory network,and a neural signal marker based on the fusion feature is established,which provides a judgment index for realizing the judgment of the patient’s state during the operation.The experimental results show that the method can accurately monitor the anesthesia state and has a higher numerical metric in the classification of the awake state and the unconscious state of the EEG in the hippocampus.