ArticlePhysical and engineering sciences in medicine2022
Developing a robust model to predict depth of anesthesia from single channel EEG signal.
Article in Physical and engineering sciences in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Deep learning based depth of anaesthesia monitoring using EEG: a 4-layer CNN model with PSD and BSR correlation features.Physical and engineering sciences in medicine · 2026Article
- Predicting depth of anaesthesia from single-channel EEG using a deep TCN-BiLSTM-attention model with EWMA.Scientific reports · 2026Article
- AnesNet: a versatile deep convolutional framework for predicting depth of anesthesia using BIS and multimodal intraoperative signals.BMC anesthesiology · 2026Article
- Leveraging advanced graph neural networks for the enhanced classification of post anesthesia states to aid surgical procedures.PloS one · 2025Article
- Accurate depth of anesthesia monitoring based on EEG signal complexity and frequency features.Brain informatics · 2024Article
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2 authors.
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Abstract
Monitoring depth of anaesthesia (DoA) from electroencephalograph (EEG) signals is an ongoing challenge for anaesthesiologists. In this study, we propose an intelligence model that predicts the DoA from a single channel electroencephalograph (EEG) signal. A segmentation technique based on a sliding window is employed to partition EEG signals. Hierarchical dispersion entropy (HDE) is applied to each EEG segment. A set of features is extracted from each EEG segment. The extracted features are investigated using a community graph detection approach (CGDA), and the most relevant features are selected to trace the DoA. The proposed model, based on HDE coupled with CGDA, is evaluated in term of BIS index using several statistical metrics such Q-Q plot, regression, and correlation coefficients. In addition, the proposed model is evaluated against the BIS index in the case of the poor signal quality. The results demonstrated that the proposed model showed an earlier reaction compared with the BIS index when patient's state transits from deep anaesthesia to moderate anaesthesia in the case of poor signal quality. The highest Pearson correlation coefficient obtained by the proposed is 0.96.
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