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DOI 10.1155/2022/4696128

Медицинская библиотека · Journal of healthcare engineering · 2022

Effect of Carbon Dioxide on Bispectral Index of EEG under Intravenous Target-Controlled Anesthesia Based on Intelligent Medical Treatment.

Li Aizhi, He Qunhui, Li Rulin, Chen Yu, Xu Weiwei

СтатьяОтозванная публикацияПеревод пока не подготовлен
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Аннотация

Laparoscopic surgery has the advantages of less trauma and quick recovery, and it is more and more favored by surgeons and patients in clinical practice. However, the impact of carbon dioxide pneumoperitoneum on the body during laparoscopic surgery has attracted the attention of many scholars. Pneumoperitoneum can cause increased cerebral blood flow and increased intracranial pressure, cerebral metabolic rate is highly correlated with blood carbon dioxide partial pressure, and cerebral metabolism without cardiopulmonary bypass is linearly correlated with the depth of anesthesia. Electroencephalographic (EEG) bispectral index (BIS) is a signal analysis method, which can directly measure the effect of drugs on the cerebral cortex and reflect the depth of anesthesia. Based on this, this study takes smart medical treatment as the background and uses the improved BP neural network as a tool to explore the effect of carbon dioxide on EEG bispectral index under intravenous target-controlled anesthesia. The main purpose is to observe the correlation between arterial blood carbon dioxide partial pressure and EEG bispectral index under propofol target-controlled anesthesia during retroperitoneal laparoscopic surgery. The experimental results show that the model proposed in this study can efficiently and accurately obtain the size of the influencing factors, which provides a clinical basis for the anesthesia management and anesthesia depth regulation of carbon dioxide pneumoperitoneum laparoscopic surgery.

Для цитирования: Li Aizhi, He Qunhui, Li Rulin, Chen Yu, Xu Weiwei. Effect of Carbon Dioxide on Bispectral Index of EEG under Intravenous Target-Controlled Anesthesia Based on Intelligent Medical Treatment.. Journal of healthcare engineering. 2022. DOI: 10.1155/2022/4696128.

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