Data_Sheet_2_Unconscious classification of quantitative electroencephalogram features from propofol versus propofol combined with etomidate anesthesia using one-dimensional convolutional neural network.docx

ObjectiveEstablishing a convolutional neural network model for the recognition of characteristic raw electroencephalogram (EEG) signals is crucial for monitoring consciousness levels and guiding anesthetic drug administration.MethodsThis trial was conducted from December 2023 to March 2024. A total of 40 surgery patients were randomly divided into either a propofol group (1% propofol injection, 10 mL: 100 mg) (P group) or a propofol-etomidate combination group (1% propofol injection, 10 mL: 100 mg, and 0.2% etomidate injection, 10 mL: 20 mg, mixed at a 2:1 volume ratio) (EP group). In the P group, target-controlled infusion (TCI) was employed for sedation induction, with an initial effect site concentration set at 5–6 μg/mL. The EP group received an intravenous push with a dosage of 0.2 mL/kg. Six consciousness-related EEG features were extracted from both groups and analyzed using four prediction models: support vector machine (SVM), Gaussian Naive Bayes (GNB), artificial neural network (ANN), and one-dimensional convolutional neural network (1D CNN). The performance of the models was evaluated based on accuracy, precision, recall, and F1-score.ResultsThe power spectral density (94%) and alpha/beta ratio (72%) demonstrated higher accuracy as indicators for assessing consciousness. The classification accuracy of the 1D CNN model for anesthesia-induced unconsciousness (97%) surpassed that of the SVM (83%), GNB (81%), and ANN (83%) models, with a significance level of p

旨在建立一种卷积神经网络模型以识别特征原始脑电图（EEG）信号，对于监测意识水平及指导麻醉药物给药至关重要。研究方法：本试验于2023年12月至2024年3月进行，共纳入40例手术患者，随机分为丙泊酚组（丙泊酚注射，1%，10 mL：100 mg）（P组）或丙泊酚-依托咪酯联合组（丙泊酚注射，1%，10 mL：100 mg，依托咪酯注射，0.2%，10 mL：20 mg，按2:1体积比混合）（EP组）。在P组中，采用目标控制输注（TCI）进行镇静诱导，初始效应部位浓度为5–6 μg/mL。EP组接受0.2 mL/kg的静脉推注。从两组中提取了六项与意识相关的EEG特征，并使用四种预测模型进行分析：支持向量机（SVM）、高斯朴素贝叶斯（GNB）、人工神经网络（ANN）和一维卷积神经网络（1D CNN）。根据准确率、精确度、召回率和F1分数评估模型的性能。研究结果：功率谱密度（94%）和α/β比值（72%）作为评估意识水平的指标，其准确性较高。对于麻醉诱导的无意识状态，1D CNN模型的分类准确率（97%）超过了SVM（83%）、GNB（81%）和ANN（83%）模型，且具有显著性的p值。