ArticleOpen life sciences2023
Monitoring and evaluation of anesthesia depth status data based on neuroscience.
Article in Open life sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Monitoring and analysis of anesthesia depth status data refers to evaluating the anesthesia depth status of patients during the surgical process by monitoring their physiological index data, and conducting analysis and judgment. The depth of anesthesia is crucial for the safety and success of the surgical process. By monitoring the state of anesthesia depth, abnormal conditions of patients can be detected in a timely manner and corresponding measures can be taken to prevent accidents from occurring. Traditional anesthesia monitoring methods currently include computer tomography, electrocardiogram, respiratory monitoring, etc. In this regard, traditional physiological indicator monitoring methods have certain limitations and cannot directly reflect the patient's neural activity status. The monitoring and analysis methods based on neuroscience can obtain more information from the level of brain neural activity. PURPOSE: In this article, the monitoring and analysis of anesthesia depth status data would be studied through neuroscience.
methodsThrough a controlled experiment, the monitoring accuracy of traditional anesthesia status monitoring algorithm and neuroscience-based anesthesia status monitoring algorithm was studied, and the information entropy and oxygen saturation of electroencephalogram signals in patients with different anesthesia depth were explored.
resultsThe experiment proved that the average monitoring accuracy of the traditional anesthesia state monitoring algorithm in patients' blood drug concentration and oxygen saturation reached 95.55 and 95.00%, respectively. In contrast, the anesthesia state monitoring algorithm based on neuroscience performs better, with the average monitoring accuracy of drug concentration and oxygen saturation reaching 98.00 and 97.09%, respectively. This experimental result fully proved that the monitoring performance of anesthesia state monitoring algorithms based on neuroscience is better.
conclusionThe experiment proved the powerful monitoring ability of the anesthesia state monitoring algorithm based on neuroscience used in this article, and explained the changing trend of brain nerve signals and oxygen saturation of patients with different anesthesia depth states, which provided a new research method for the monitoring and analysis technology of anesthesia depth state data.
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