cinc2023
Predicting Neurological Recovery from Coma After Cardiac Arrest: The George B. Moody PhysioNet Challenge 2023
Science Score: 41.0%
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✓CITATION.cff file
Found CITATION.cff file -
✓codemeta.json file
Found codemeta.json file -
○.zenodo.json file
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○DOI references
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✓Academic publication links
Links to: ieee.org -
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○Scientific vocabulary similarity
Low similarity (10.2%) to scientific vocabulary
Repository
Predicting Neurological Recovery from Coma After Cardiac Arrest: The George B. Moody PhysioNet Challenge 2023
Basic Info
- Host: GitHub
- Owner: DeepPSP
- License: mit
- Language: Python
- Default Branch: master
- Size: 11.6 MB
Statistics
- Stars: 2
- Watchers: 3
- Forks: 0
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
CinC2023
Predicting Neurological Recovery from Coma After Cardiac Arrest: The George B. Moody PhysioNet Challenge 2023
- The Conference
- Description of the files/folders(modules)
- Distributions of the EEG data against clinical information of the patients
- External Resources Used
The Conference
Conference Website | Official Phase Leaderboard | Final Results
The table of final results of the team:
Click to view the table of final results
Final results collecting:
```python from utils.gatherresults import gatherresults
td = getteamdigest("Revenger") # overall digest
a smaller part of the overall digest, in the format of a latex table
Challenge Score will always be included in the table in the front rows
td = getteamdigest("Revenger", fmt="tex", hour_limits=[72, 48, 24], targets=["CPC"], metrics=["MAE"]) ```
Click to view the leaderboard
Click to view the conference poster
Conference paper: GitHub | IEEE Xplore | [CinC Papers On-line](https://cinc.org/archives/2023/pdf/CinC2023-060.pdf)
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Description of the files/folders(modules)
Files
Click to view the details
- [README.md](README.md): this file, serves as the documentation of the project. - [cfg_models.py](cfg_models.py), [cfg.py](cfg.py): configuration files (the former for configuration of models, the latter for configuration of the whole project) - [data_reader.py](data_reader.py): data reader, including data downloading, file listing, data loading, etc. - [dataset.py](dataset.py): dataset class, which feeds data to the models. - [Dockerfile](Dockerfile): docker file for building the docker image for submissions. - [evaluate_model.py](evaluate_model.py), [helper_code.py](helper_code.py), [remove_data.py](remove_data.py), [remove_labels.py](remove_labels.py), [run_model.py](run_model.py), [train_model.py](train_model.py), [truncate_data.py](truncate_data.py): scripts inherited from the [official baseline](https://github.com/physionetchallenges/python-example-2023.git) and [official scoring code](https://github.com/physionetchallenges/evaluation-2023.git). Modifications on these files are invalid and are immediately overwritten after being pulled by the organizers (or the submission system). - [evaluate_pipeline.py](evaluate_pipeline.py): pipeline for evaluating models on multiple patients. - [sync_official.py](sync_official.py）: script for synchronizing data from the official baseline and official scoring code. - [requirements.txt](requirements.txt), [requirements-docker.txt](requirements-docker.txt), [requirements-no-torch.txt](requirements-no-torch.txt): requirements files for different purposes. - [team_code.py](team_code.py): entry file for the submissions. - [test_local.py](test_local.py), [test_docker.py](test_docker.py), [test_run_challenge.sh](test_run_challenge.sh): scripts for testing the docker image and the local environment. The latter 2 files along with the [docker-test action](.github/workflows/docker-test.yml) are used for CI. Passing the CI almost guarantees that the submission will run successfully in the official environment, except for potential GPU-related issues (e.g. model weights and data are on different devices, i.e. CPU and GPU, in which case torch will raise an error). - [trainer.py](trainer.py): trainer class, which trains the models. - [submissions](submissions): log file for the submissions, including the key hyperparameters, the scores received, commit hash, etc. The log file is updated after each submission and organized as a YAML file.Folders(Modules)
Click to view the details
- [official_baseline](official_baseline): the official baseline code, included as a submodule. - [official_scoring_metric](official_scoring_metric): the official scoring code, included as a submodule. - [models](models): folder for model definitions, including [CRNN models](models/crnn.py), and [traditional ML models](models/ml.py). The latter serves as a minimal garantee model using patient metadata only, which is used when no (EEG) data is available. It is indeed a wrapper containing model construction, training, hyperparameter tuning via grid search, model saving/loading, and end-to-end inference (from raw input to the form of output that the challenge requires). - [utils](utils): various utility functions, as well as some intermediate data files (e.g. train-val split files, etc.). SQI computation code, as mentioned in the unofficial phase (and also the [v1 version of the I-CARE database](https://physionet.org/content/i-care/1.0/)). This will be described in detail in the [External Resources Used](#external-resources-used) section.:point_right: Back to TOC
Distributions of the EEG data against clinical information of the patients
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External Resources Used
SQI (Signal Quality Index) Calculation
Source Code integrated from bdsp-core/icare-dl.
As stated in the Artfiact Screening (Signal Quality) subsection of the Data Description section of the
I-CARE database version 1.0 hosted at PhysioNet, the SQI is calculated as follows:
...This artifact score is based on how many 10-second epochs within a 5-minute EEG window are contaminated by artifacts. Each 10-second epoch was scored for the presence of the following artifacts including: 1) flat signal, 2) extreme high or low values, 3) muscle artifact, 4) non-physiological spectra, and 5) implausibly fast rising or decreasing signal amplitude...
Precomputed SQI (5min window (epoch), 1min step length) for all EEGs: Google Drive | Alternative
Distribution of SQI for all 5min windows (epochs):
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Owner
- Name: DeepPSP
- Login: DeepPSP
- Kind: organization
- Location: China
- Repositories: 15
- Profile: https://github.com/DeepPSP
deep learning for physiological signal processing
Citation (CITATIONS.bib)
@inproceedings{Kang_cinc2023,
title = {{Predicting Neurological Recovery from Coma with Longitudinal Electroencephalogram Using Deep Neural Networks}},
author = {Kang, Jingsu and WEN, Hao},
booktitle = {{Computing in Cardiology Conference (CinC)}},
series = {{CinC2023}},
issn = {2325-887X},
doi = {10.22489/cinc.2023.060},
publisher = {{Computing in Cardiology}},
year = {2023},
month = {11},
location = {{Atlanta, GA, USA}},
collection = {{CinC2023}}
}
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