信号处理和机器学习算法对麻醉深度进行分类。

IF 4.1 Q1 HEALTH CARE SCIENCES & SERVICES
Oscar Mosquera Dussan, Eduardo Tuta-Quintero, Daniel A Botero-Rosas
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引用次数: 0

摘要

背景:对麻醉深度(AD)的评估不佳导致麻醉剂过量或服用不足,这需要持续监测以避免并发症。对中枢神经系统活动和自主神经系统的评估可以为外科手术期间AD的监测提供额外的信息。方法:观察分析单中心研究,在全麻下的手术过程中收集生物信号信息,进行信号预处理、处理和后处理,以提供模式分类器并确定患者的AD状态。使用MATLAB V.8.1通过数据处理和算法开发来开发脑电图指标。结果:共有25名男性和35名男性 包括女性,手术总时间平均为109.62 min.结果显示复杂性脑电波指数与熵模块的指数之间具有高Pearson相关性。在状态熵和反应熵指数中观察到更大的离散性,在这些指数中与深度麻醉和全身麻醉相关的框中也可以看到部分重叠。高Pearson相关性可以通过与清醒和全身麻醉状态相对应的重合值来解释。高Pearson相关性可以通过与清醒和全身麻醉状态相对应的重合值来解释。结论:生物信号滤波和机器学习算法可用于外科手术中的AD分类。需要进一步的研究来证实这些结果,并改善麻醉师在全身麻醉中的决策。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Signal processing and machine learning algorithm to classify anaesthesia depth.

Signal processing and machine learning algorithm to classify anaesthesia depth.

Signal processing and machine learning algorithm to classify anaesthesia depth.

Signal processing and machine learning algorithm to classify anaesthesia depth.

Background: 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.

Methods: 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.

Results: 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.

Conclusion: 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.

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来源期刊
CiteScore
6.10
自引率
4.90%
发文量
40
审稿时长
18 weeks
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