Estimating the Depth of Anesthesia from EEG Signals Based on a Deep Residual Shrinkage Network
Abstract
:1. Introduction
2. Materials and Methods
2.1. Dataset
2.2. EEG Signals Preprocessing
2.3. Evaluation Metrics
2.4. Deep Learning Model
2.4.1. Deep Residual Shrinkage Network
2.4.2. 1 × 1 Convolution
2.4.3. Proposed Regression Model
2.5. Conventional Models
2.5.1. Features Extraction
- Band Power
- Spectral Edge Frequency
- Sample Entropy
2.5.2. Conventional Regression Models
- Support Vector Machine
- Random Forest
- Artificial Neural Network
3. Results
3.1. Experimental Settings
3.2. Experimental Results
4. Discussion
- The recorded raw EEG signals are usually contaminated by electrical noise and other physiological signals. We used bandpass finite filters to remove electrical noise, and the WT-CEEMDAN-ICA algorithm to extract clean EEG signals.
- We adopted deep learning models to extract discriminative features automatically instead of extracting features manually from EEG signals.
- To improve our proposed model’s generalization ability and convergence speed, we standardized the EEG signals.
- DRSN-CW can deal with signals disturbed by noise, which is suitable for EEG-signal processing.
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Metric | Formula | Description |
---|---|---|
MSE (Regression) | Mean Squared Error | |
ACC (Classification) | Accuracy | |
SE (Classification) | Sensitivity | |
PR (Not used directly in this paper) | Precision | |
F1 (Classification) | F1-score |
Metrics | SVR | RF | ANN | Our Proposed Model |
---|---|---|---|---|
MSE | 166.02 ± 7.77 | 90.95 ± 4.88 | 109.20 ± 5.80 | 40.35 ± 3.22 |
ACC | 0.8596 ± 0.0574 | 0.8640 ± 0.0720 | 0.8606 ± 0.0380 | 0.9503 ± 0.0224 |
SE | 0.4825 ± 0.3391 | 0.6685 ± 0.1266 | 0.5650 ± 0.2801 | 0.8411 ± 0.0790 |
F1 | 0.475 ± 0.2941 | 0.6770 ± 0.0840 | 0.5901 ± 0.2337 | 0.8395 ± 0.0812 |
Metrics | SVR | RF | ANN | Our Proposed Model |
---|---|---|---|---|
MSE | 173.22 ± 8.56 | 97.56 ± 6.88 | 133.49 ± 5.40 | 49.22 ± 4.62 |
ACC | 0.7908 ± 0.1187 | 0.8420 ± 0.0765 | 0.8216 ± 0947 | 0.9203 ± 0.0470 |
SE | 0.4675 ± 0.3391 | 0.6575 ± 0.1266 | 0.5700 ± 0.1414 | 0.8054 ± 0.0243 |
F1 | 0.4599 ± 0.2132 | 0.6670 ± 0.0821 | 0.5852 ± 0.1274 | 0.8070 ± 0.0306 |
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Shi, M.; Huang, Z.; Xiao, G.; Xu, B.; Ren, Q.; Zhao, H. Estimating the Depth of Anesthesia from EEG Signals Based on a Deep Residual Shrinkage Network. Sensors 2023, 23, 1008. https://doi.org/10.3390/s23021008
Shi M, Huang Z, Xiao G, Xu B, Ren Q, Zhao H. Estimating the Depth of Anesthesia from EEG Signals Based on a Deep Residual Shrinkage Network. Sensors. 2023; 23(2):1008. https://doi.org/10.3390/s23021008
Chicago/Turabian StyleShi, Meng, Ziyu Huang, Guowen Xiao, Bowen Xu, Quansheng Ren, and Hong Zhao. 2023. "Estimating the Depth of Anesthesia from EEG Signals Based on a Deep Residual Shrinkage Network" Sensors 23, no. 2: 1008. https://doi.org/10.3390/s23021008