Journal Article

Accurate depth of anesthesia monitoring based on EEG signal complexity and frequency features

Wen, Peng, Huang, Yi, Li, Tianning, Li, Yan

2024

Overview

Accurate monitoring of the depth of anesthesia (DoA) is essential for ensuring patient safety and effective anesthesia management. Existing methods, such as the Bispectral Index (BIS), are limited in real-time accuracy and robustness. Current methods have problems in generalizability across diverse patient datasets and are sensitive to artifacts, making it difficult to provide reliable DoA assessments in real time. This study proposes a novel method for DoA monitoring using EEG signals, focusing on accuracy, robustness, and real-time application. EEG signals were pre-processed using wavelet denoising and discrete wavelet transform (DWT). Features such as Permutation Lempel–Ziv Complexity (PLZC) and Power Spectral Density (PSD) were extracted. A random forest regression model was employed to estimate anesthetic states, and an unsupervised learning method using the Hurst exponent algorithm and hierarchical clustering was introduced to detect transitions between anesthesia states. The method was tested on two independent datasets (UniSQ and VitalDB), achieving an average Pearson correlation coefficient of 0.86 and 0.82, respectively. For the combined dataset, the model demonstrated an R-squared value of 0.70, a RMSE of 6.31, a MAE of 8.38, and a Pearson correlation of 0.84, showcasing its robustness and generalizability. This approach offers a more accurate and reliable real-time DoA monitoring tool that could significantly improve patient safety and anesthesia management, especially in diverse clinical environments.

• Publisher: Springer Berlin Heidelberg,Springer,Springer Nature B.V,SpringerOpen
• Subject: Alfentanil / Algorithms / Anesthesia / Artificial Intelligence / Care and treatment / Cluster analysis / Clustering / Cognitive Psychology / Complexity / Computation by Abstract Devices / Computer Science / Correlation coefficients / Datasets / Depth of anesthesia (DoA) / Discrete Wavelet Transform / Electroencephalogram (EEG) / Electroencephalography / Health Informatics / Hierarchical clustering / Hurst exponent algorithm / Machine learning / Machine Learning Techniques for Neuroscience Big Data / Monitoring / Neurophysiology / Neurosciences / Patient safety / Patients / Permutation Lempel–Ziv Complexity (PLZC) / Permutations / Power spectral density / Power spectral density (PSD) / Real time / Regression models / Robustness / Safety management / Unsupervised learning / Wavelet transforms