Accurate depth of anesthesia monitoring based on EEG signal complexity and frequency features
Article
Article Title | Accurate depth of anesthesia monitoring based on EEG signal complexity and frequency features |
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ERA Journal ID | 211938 |
Article Category | Article |
Authors | Li, Tianning, Huang, Yi, Wen, Paul and Li, Yan |
Journal Title | Brain Informatics |
Journal Citation | 11 |
Article Number | 28 |
Number of Pages | 20 |
Year | 2024 |
Publisher | Springer |
Place of Publication | Germany |
ISSN | 2198-4018 |
2198-4026 | |
Digital Object Identifier (DOI) | https://doi.org/10.1186/s40708-024-00241-y |
Web Address (URL) | https://link.springer.com/article/10.1186/s40708-024-00241-y |
Abstract | 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 |
Keywords | Power spectral density (PSD); Random forest regression; Hierarchical clustering; Depth of anesthesia (DoA); Electroencephalogram (EEG); Permutation Lempel–Ziv Complexity (PLZC); Hurst exponent algorithm |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 460299. Artificial intelligence not elsewhere classified |
400399. Biomedical engineering not elsewhere classified | |
Byline Affiliations | School of Mathematics, Physics and Computing |
School of Engineering |
https://research.usq.edu.au/item/zq8y1/accurate-depth-of-anesthesia-monitoring-based-on-eeg-signal-complexity-and-frequency-features
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