基于时间序列特征提取和机器学习的麻醉脑电信号深度分析

Decis. Sci. Pub Date : 2023-05-05 DOI:10.3390/sci5020019
Raghav V. Anand, M. Abbod, S. Fan, J. Shieh
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引用次数: 0

摘要

“麻醉深度”一词是指全身麻醉剂在给药时以特定强度浓度使中枢神经系统镇静的程度。麻醉深度对手术并发症的发生起着至关重要的作用,控制麻醉深度是手术成功的必要条件。本研究使用脑电图(EEG)信号来预测麻醉的深度。使用传统的预处理方法,如信号分解和基于深度学习的模型构建,对麻醉深度进行分类。本文提出了一种基于时间序列特征提取的麻醉程度分类方法,通过寻找一段时间内脑电信号与双谱指数之间的关系。通过分析脑电信号与双谱指数之间的关系,在可扩展假设检验的基础上进行时间序列特征提取,提取特征,并利用支持向量分类器、XG boost分类器、梯度boost分类器、决策树和随机森林分类器等机器学习模型训练特征,预测麻醉深度。训练最好的模型是随机森林,准确率为83%。这为该领域基于时间序列的特征提取的进一步研究和深入提供了平台。
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Depth Analysis of Anesthesia Using EEG Signals via Time Series Feature Extraction and Machine Learning
The term “anesthetic depth” refers to the extent to which a general anesthetic agent sedates the central nervous system with specific strength concentration at which it is delivered. The depth level of anesthesia plays a crucial role in determining surgical complications, and it is imperative to keep the depth levels of anesthesia under control to perform a successful surgery. This study used electroencephalography (EEG) signals to predict the depth levels of anesthesia. Traditional preprocessing methods such as signal decomposition and model building using deep learning were used to classify anesthetic depth levels. This paper proposed a novel approach to classify the anesthesia levels based on the concept of time series feature extraction, by finding out the relation between EEG signals and the bi-spectral Index over a period of time. Time series feature extraction on basis of scalable hypothesis tests were performed to extract features by analyzing the relation between the EEG signals and Bi-Spectral Index, and machine learning models such as support vector classifier, XG boost classifier, gradient boost classifier, decision trees and random forest classifier are used to train the features and predict the depth level of anesthesia. The best-trained model was random forest, which gives an accuracy of 83%. This provides a platform to further research and dig into time series-based feature extraction in this area.
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