| --- |
| language: |
| - en |
| license: odc-by |
| tags: |
| - medical |
| - eeg |
| - tabular-classification |
| - seizure-prediction |
| metrics: |
| - accuracy |
| - f1 |
| - roc_auc |
| --- |
| |
| # Neuro Pulse Model |
|
|
| This repository contains trained models for predicting epileptic seizures using EEG feature data derived from the CHB-MIT Scalp EEG Database. |
|
|
| Research prototype. Not a medical device; not for clinical decisions. |
|
|
| Code: https://github.com/JayD108/Neuro_Pulse |
| |
| > **Data provenance:** The EEG feature data in this repository is derived from the CHB-MIT Scalp EEG Database v1.0.0, distributed by PhysioNet under the Open Data Commons Attribution License v1.0 (ODC-By 1.0). This repository does not contain or redistribute the original `.edf` recordings. See the [License / Data Provenance](#license--data-provenance) section below. |
| |
| ## Models |
| - models/trained/apala_xgb_v1.json: XGBoost (500 trees, depth 6) trained on all 24 patients, 590 features, |
| XGBoost native format (no pickle). Used by the APALA dashboard. |
| Leave-one-patient-out ROC-AUC: mean 0.56, median 0.59, range 0.26–0.71 (per patient: apala_xgb_v1_lopo.json). |
| - models/trained/xgboost_model.pkl: original V1 XGBoost, 180 features, trained on chb01 only. |
| ROC-AUC 0.998 on chb01 itself; it does not generalise to other patients. |
| - master_model.pkl / ensemble_model.pkl: V1 stacking ensembles (research). Need scikit-learn to load. |
| |
| ## Data Information |
| |
| The processed CSVs are in the dataset repo https://huggingface.co/datasets/JayF14/chb-mit-preprocessed-eeg, not in this repo. |
| |
| - The features are provided in CSV format (`chbXX_labeled_features.csv`). |
| - The original raw `.edf` EEG recordings are **not** included here; they are available from PhysioNet: https://physionet.org/content/chbmit/1.0.0/ |
| |
| ## How to Use |
| |
| Load the APALA model: |
| |
| ```python |
| from huggingface_hub import hf_hub_download |
| import xgboost as xgb |
|
|
| path = hf_hub_download("JayF14/Neuro_Pulse", "models/trained/apala_xgb_v1.json") |
| model = xgb.XGBClassifier() |
| model.load_model(path) |
| ``` |
| |
| ## License / Data Provenance |
| |
| The EEG feature data in this repository is a **processed/feature-engineered derivative** of the **CHB-MIT Scalp EEG Database v1.0.0**. |
| |
| - **Original dataset:** CHB-MIT Scalp EEG Database v1.0.0 |
| - **PhysioNet page:** https://physionet.org/content/chbmit/1.0.0/ |
| - **DOI:** 10.13026/C2K01R |
| - **Source license:** Open Data Commons Attribution License v1.0 (ODC-By 1.0) |
| - License text: https://opendatacommons.org/licenses/by/1.0/ |
| |
| The creator of this repository does **not** claim ownership of the underlying EEG recordings. The original CHB-MIT EEG recordings are distributed by PhysioNet under the Open Data Commons Attribution License v1.0. |
| |
| When using or redistributing this work, please include the following attribution: |
| |
| > This work uses data derived from the CHB-MIT Scalp EEG Database v1.0.0 (Guttag, 2010), distributed by PhysioNet (https://physionet.org/content/chbmit/1.0.0/) under the Open Data Commons Attribution License v1.0 (https://opendatacommons.org/licenses/by/1.0/). DOI: 10.13026/C2K01R. |
| |
| ## Citation |
| |
| ```bibtex |
| @article{guttag2010chbmit, |
| author = {Guttag, John}, |
| title = {{CHB-MIT Scalp EEG Database}}, |
| journal = {{PhysioNet}}, |
| year = {2010}, |
| month = jun, |
| note = {Version 1.0.0}, |
| doi = {10.13026/C2K01R}, |
| url = {https://doi.org/10.13026/C2K01R} |
| } |
| ``` |
| |
