An Intraoperative EEG Biomarker for Postoperative Delirium Predicting Based on Interpretable Deep Learning Framework.
The 8 matches
- [1] § Results › Best Temporal Filter Captures an EEG Signature of POD Individuals ↔ plot/Figure4.ipynb, lines 254–318 · score 0.69 · gradient boosting machine, multilayer perceptron, random forest, 10 %, training, accuracies
- [2] § Results › Best Temporal Filter Captures an EEG Signature of POD Individuals ↔ plot/figure_3.ipynb, lines 282–359 · score 0.69 · gradient boosting machine, multilayer perceptron, random forest, 10 %, training, accuracies
- [3] § Materials and Methods › Filter Analysis ↔ plot/Figure4.ipynb, lines 254–318 · score 0.68 · multilayer perceptron, gradient boosting, random forest, training, model
- [4] § Materials and Methods › Filter Analysis ↔ plot/figure_3.ipynb, lines 282–359 · score 0.68 · multilayer perceptron, gradient boosting, random forest, training, model
- [5] § Results › A Novel EEG Waveform as a Potential Biomarker for POD Early Warning ↔ plot/Figure5.ipynb, lines 187–248 · score 0.63 · power spectral density, confidence interval, target wave, 12 Hz
- [6] § Results › A Novel EEG Waveform as a Potential Biomarker for POD Early Warning ↔ plot/Figure5.ipynb, lines 561–662 · score 0.58 · biomarker events, confidence intervals, brain regions, bootstrap, min, POD
- [7] § Results › A Novel EEG Waveform as a Potential Biomarker for POD Early Warning ↔ plot/Figure5.ipynb, lines 561–662 · score 0.53 · confidence interval, brain regions, event, bootstrap, min, biomarkers
- [8] § Results › A Novel EEG Waveform as a Potential Biomarker for POD Early Warning ↔ supp_plot/supp_figure_4.ipynb, lines 74–140 · score 0.52 · power spectral density, target wave, PSD, POD, 12 Hz
Paper
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The authors' code
Jupyter notebook · 662 lines · 522 KB · no license · 3 matches
Figure5.ipynb at commit 0f9c56d, no license · at the source
Overview
- Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, Guangdong, China
- Department of Anesthesiology and Perioperative Medicine, Xijing Hospital, The Fourth Military Medical University, Xi'an, China
- Key Laboratory of Anesthesiology (The Fourth Military Medical University), Ministry of Education, Xi'an, China
- Shaanxi Provincial Clinical Research Center for Anesthesiology Medicine, Xi'an, China
- Department of Anesthesiology, The Third People's Hospital of Chengdu, Chengdu, Sichuan, China
- Center for Neurocognition and Social Behavior, Artificial Intelligence Research Institute, Shenzhen University of Advanced Technology, Shenzhen, Guangdong, China
Abstract
Postoperative delirium (POD) is a common complication in older surgical patients and substantially worsens clinical outcomes, yet existing intraoperative electroencephalography (EEG) monitoring tools lack spatial and temporal specificity, creating a need for interpretable biomarkers. We prospectively analyzed 32‐channel intraoperative EEG from 71 patients aged ≥ 60 undergoing noncardiac surgery, trained an interpretable spatiotemporal convolutional network (ST‐CN), derived a best temporal filter (BTF), and evaluated model performance with region‐specific tests and independent external validation. The ST‐CN classified POD with 97.52% accuracy and an ROC of 0.996. The BTF alone discriminated POD with an AUC of 0.911 and achieved 85.12% accuracy using frontal EEG alone. It captured a distinct 2–12 Hz (δ–θ–α) oscillation in a spindle‐like envelope, which occurred at a significantly higher rate in POD patients (4.62 ± 0.15 vs. 3.88 ± 0.13 waves/
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ncclab-sustech/POD-biomarker
0f9c56db9a968774970f1494373a6581407b2c88, 13 September 2025Availability: 1 check, the latest on 26 September 2026: the link answers
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Figure4.ipynb — Jupyter, 402 lines, 2 matches, shown from its source - plot/
Figure5.ipynb — Jupyter, 662 lines, 3 matches, shown from its source - plot/
figure_3.ipynb — Jupyter, 953 lines, 2 matches, shown from its source - supp_plot/
supp_figure_1.ipynb — Jupyter, 138 lines, shown from its source - supp_plot/
supp_figure_2.ipynb — Jupyter, 257 lines, shown from its source - supp_plot/
supp_figure_3.ipynb — Jupyter, 288 lines, shown from its source - supp_plot/
supp_figure_4.ipynb — Jupyter, 208 lines, 1 match, shown from its source - train/
SubjectDataset.py — Python, 206 lines, shown from its source - train/
compute contribution.py — Python, 395 lines, shown from its source - train/
model.py — Python, 366 lines, shown from its source - train/
spindle_detection.ipynb — Jupyter, 204 lines, shown from its source - train/
train_main.py — Python, 291 lines, shown from its source - train/
train_utils.py — Python, 765 lines, shown from its source - README.md — Text, 1 line, shown from its source
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Data
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Data Availability Statement
The analysis code has been uploaded to GitHub, the code is available at https://
Reproduced under the paper's license (CC BY), from the paper cited above.
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Version 3, 28 September 2026
- Funding: added National Natural Science Foundation of China: 2021ZD0200500, 82221001, 82271211, 82293643, 62472206, 82430040, 3254100307; Southern University of Science and Technology; Shenzhen Municipal Science and Technology Innovation Council: KJZD20230923115221044, RCBS20231211090748082; National Science and Technology Major Project: 2021ZD0200500, 2025ZD0218300; Basic and Applied Basic Research Foundation of Guangdong Province: 2025A1515011645, 2026A1515010121, 2026B1515020099
Version 1, 27 September 2026: the first record
Recorded: type, language, journal, volume, issue, pages, dates, 12 authors, 4 keywords, 36 references.
Cite
This paper
Zhang, Y., Zhu, Y., Zhang, X., Shen, X., Tang, X., Lu, Z., Lei, C., Li, M., Dong, H., Liang, Z., Liu, Q., & Zhao, G. (2026). An Intraoperative EEG Biomarker for Postoperative Delirium Predicting Based on Interpretable Deep Learning Framework. MedComm, 7(9), e70980. https://
BibTeX
@article{zhang2026intrao
author = {Zhang, Yinuo and Zhu, Yan and Zhang, Xinxin and Shen, Xinke and Tang, Xuemiao and Lu, Zhihong and Lei, Chong and Li, Mengyu and Dong, Hailong and Liang, Zhichao and Liu, Quanying and Zhao, Guangchao},
title = {{An Intraoperative EEG Biomarker for Postoperative Delirium Predicting Based on Interpretable Deep Learning Framework}},
journal = {MedComm},
year = {2026},
month = sep,
volume = {7},
number = {9},
pages = {e70980},
publisher = {Wiley},
issn = {2688-2663},
doi = {10.1002/
url = {https://
pmid = {42707129},
pmcid = {PMC13547085}
}
RIS
TY - JOUR
AU - Zhang, Yinuo
AU - Zhu, Yan
AU - Zhang, Xinxin
AU - Shen, Xinke
AU - Tang, Xuemiao
AU - Lu, Zhihong
AU - Lei, Chong
AU - Li, Mengyu
AU - Dong, Hailong
AU - Liang, Zhichao
AU - Liu, Quanying
AU - Zhao, Guangchao
TI - An Intraoperative EEG Biomarker for Postoperative Delirium Predicting Based on Interpretable Deep Learning Framework
T2 - MedComm
J2 - MedComm (2020)
PY - 2026
DA - 2026/
VL - 7
IS - 9
SP - e70980
SN - 2688-2663
PB - Wiley
DO - 10.1002/
UR - https://
LA - en
ER -
CSL-JSON
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