This study aims to estimate the depth of anesthesia (DOA) at a safe and appropriate level taking into account the patient characteristics during the induction phase. Bi-spectral Index signal (BIS) as a common approach of controlling DOA generates noise and delays in the initial phase of induction. This may lead to useless information in the process of controlling. Moreover, using the BIS index entails a time-consuming process, high equipping costs, and a lack of accessibility to device accessories. To overcome these problems, we propose a new model of controlling DOA with no need for the use of such an index. Hence, an estimation strategy for DOA is developed applying a feedforward neural network and an adaptive neuro-fuzzy inference estimation model. This model estimates the dose of intravenous anesthetic drugs concerning the patients’ needs resulting in optimal drug dose and stable anesthesia depth. The proposed estimations are tested by sensitivity analysis being compared with real data obtained from the classical model (PK-PD) revised approach and BIS approach on 13 patients undergoing surgery. The results show an accuracy of 0.999, indicative of a high-validated model. Compared to BIS, our proposed model not only controls DOA accurately but also achieves outcomes in practice successfully. Some practical implications for future research and clinical practice are also suggested.

这项研究的目的是考虑到诱导阶段的患者特征，以安全和适当的水平估算麻醉深度（DOA）。双谱指数信号（BIS）作为控制DOA的常用方法，会在感应的初始阶段产生噪声和延迟。这可能会导致控制过程中无用的信息。此外，使用BIS索引需要耗时的过程，高昂的设备成本以及缺乏设备附件的可及性。为了克服这些问题，我们提出了一种无需使用此类索引即可控制DOA的新模型。因此，使用前馈神经网络和自适应神经模糊推理估计模型，开发了DOA的估计策略。该模型估算出与患者需求有关的静脉麻醉药剂量，从而获得最佳的药物剂量和稳定的麻醉深度。通过敏感性分析对建议的估计值进行测试，并将其与从经典模型（PK-PD）修正方法和BIS方法获得的13例接受手术的患者的真实数据进行比较。结果显示出0.999的准确性，表明该模型的有效性很高。与BIS相比，我们提出的模型不仅可以精确地控制DOA，而且可以在实践中成功取得成果。还提出了对未来研究和临床实践的一些实际意义。通过敏感性分析对建议的估计值进行测试，并将其与从经典模型（PK-PD）修正方法和BIS方法获得的13例接受手术的患者的真实数据进行比较。结果显示出0.999的准确性，表明该模型的有效性很高。与BIS相比，我们提出的模型不仅可以精确地控制DOA，而且可以在实践中成功取得成果。还提出了对未来研究和临床实践的一些实际意义。通过敏感性分析对建议的估计值进行测试，并将其与从经典模型（PK-PD）修正方法和BIS方法获得的13例接受手术的患者的真实数据进行比较。结果显示出0.999的准确性，表明该模型的有效性很高。与BIS相比，我们提出的模型不仅可以精确地控制DOA，而且可以在实践中成功取得成果。还提出了对未来研究和临床实践的一些实际意义。