Accurate depth of anesthesia monitoring based on EEG signal complexity and frequency features
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...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2024-11-01
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| Series: | Brain Informatics |
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| Online Access: | https://doi.org/10.1186/s40708-024-00241-y |
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| _version_ | 1846158089933291520 |
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| author | Tianning Li Yi Huang Peng Wen Yan Li |
| author_facet | Tianning Li Yi Huang Peng Wen Yan Li |
| author_sort | Tianning Li |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-45a6e8a57f5342f4b8d7fe78da70e5e5 |
| institution | Kabale University |
| issn | 2198-4018 2198-4026 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Brain Informatics |
| spelling | doaj-art-45a6e8a57f5342f4b8d7fe78da70e5e52024-11-24T12:48:32ZengSpringerOpenBrain Informatics2198-40182198-40262024-11-0111112010.1186/s40708-024-00241-yAccurate depth of anesthesia monitoring based on EEG signal complexity and frequency featuresTianning Li0Yi Huang1Peng Wen2Yan Li3School of Mathematics, Physics and Computing, University of Southern QueenslandSchool of Mathematics, Physics and Computing, University of Southern QueenslandSchool of Engineering, University of Southern QueenslandSchool of Mathematics, Physics and Computing, University of Southern QueenslandAbstract 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.https://doi.org/10.1186/s40708-024-00241-yDepth of anesthesia (DoA)Electroencephalogram (EEG)Permutation Lempel–Ziv Complexity (PLZC)Power spectral density (PSD)Hurst exponent algorithmHierarchical clustering |
| spellingShingle | Tianning Li Yi Huang Peng Wen Yan Li Accurate depth of anesthesia monitoring based on EEG signal complexity and frequency features Brain Informatics Depth of anesthesia (DoA) Electroencephalogram (EEG) Permutation Lempel–Ziv Complexity (PLZC) Power spectral density (PSD) Hurst exponent algorithm Hierarchical clustering |
| title | Accurate depth of anesthesia monitoring based on EEG signal complexity and frequency features |
| title_full | Accurate depth of anesthesia monitoring based on EEG signal complexity and frequency features |
| title_fullStr | Accurate depth of anesthesia monitoring based on EEG signal complexity and frequency features |
| title_full_unstemmed | Accurate depth of anesthesia monitoring based on EEG signal complexity and frequency features |
| title_short | Accurate depth of anesthesia monitoring based on EEG signal complexity and frequency features |
| title_sort | accurate depth of anesthesia monitoring based on eeg signal complexity and frequency features |
| topic | Depth of anesthesia (DoA) Electroencephalogram (EEG) Permutation Lempel–Ziv Complexity (PLZC) Power spectral density (PSD) Hurst exponent algorithm Hierarchical clustering |
| url | https://doi.org/10.1186/s40708-024-00241-y |
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