Description

Monitoring the depth of unconsciousness during anesthesia is beneficial in both clinical settings and neuroscience investigations to understand brain mechanisms. Electro encephalogram (EEG) has been used as an objective means of characterizing brain altered arousal and/or cognition states induced by anesthetics in real-time. Continuous monitoring of electroencephalogram (EEG) recordings in humans under general anesthesia (GA), local anesthesia (LA) and regional anesthesia (RA) has demonstrated that changes in EEG dynamics induced by an anesthetic drug are reliably associated with the altered arousal states caused by the drug. This observation suggests that an intelligent, closed-loop anesthesia delivery (CLAD) system operating in real-time could track EEG dynamics and control the infusion rate of a programmable pump to precisely maintain unconsciousness. In this process we proposes a deep learning algorithm to analysis the anesthesia from EEG signal. So the deep learning algorithms are Convolutional Neural Network, Long Short Term Neural Network and Artificial Neural Network will applied and analyse EEG signal and it generates the results in the form of metrics like accuracy, precision, recall and f1-score.