School of Medicine, Universidad de La Sabana, Chia, Colombia.
School of Medicine, Universidad de La Sabana, Chia, Colombia
BMJ Health Care Inform. 2023 Oct;30(1). doi: 10.1136/bmjhci-2023-100823.
Poor assessment of anaesthetic depth (AD) has led to overdosing or underdosing of the anaesthetic agent, which requires continuous monitoring to avoid complications. The evaluation of the central nervous system activity and autonomic nervous system could provide additional information on the monitoring of AD during surgical procedures.
Observational analytical single-centre study, information on biological signals was collected during a surgical procedure under general anaesthesia for signal preprocessing, processing and postprocessing to feed a pattern classifier and determine AD status of patients. The development of the electroencephalography index was carried out through data processing and algorithm development using MATLAB V.8.1.
A total of 25 men and 35 women were included, with a total time of procedure average of 109.62 min. The results show a high Pearson correlation between the Complexity Brainwave Index and the indices of the entropy module. A greater dispersion is observed in the state entropy and response entropy indices, a partial overlap can also be seen in the boxes associated with deep anaesthesia and general anaesthesia in these indices. A high Pearson correlation might be explained by the coinciding values corresponding to the awake and general anaesthesia states. A high Pearson correlation might be explained by the coinciding values corresponding to the awake and general anaesthesia states.
Biological signal filtering and a machine learning algorithm may be used to classify AD during a surgical procedure. Further studies will be needed to confirm these results and improve the decision-making of anaesthesiologists in general anaesthesia.
对麻醉深度（AD）的评估不佳导致麻醉剂过量或不足，这需要持续监测以避免并发症。评估中枢神经系统活动和自主神经系统可以为手术过程中 AD 的监测提供额外信息。
观察性分析性单中心研究，在全身麻醉下进行手术期间收集生物信号信息，用于信号预处理、处理和后处理，以馈送模式分类器并确定患者的 AD 状态。通过使用 MATLAB V.8.1 进行数据处理和算法开发，开发了脑电图指数。
共纳入 25 名男性和 35 名女性，总手术时间平均为 109.62 分钟。结果显示，复杂性脑波指数与熵模块指数之间存在高度的皮尔逊相关性。状态熵和反应熵指数的离散度较大，这些指数中与深度麻醉和全身麻醉相关的框也存在部分重叠。高皮尔逊相关性可能是由于清醒和全身麻醉状态对应的数值一致所致。
生物信号滤波和机器学习算法可用于对手术过程中的 AD 进行分类。需要进一步研究以确认这些结果并提高全身麻醉中麻醉师的决策能力。
