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ecg-ptbxl-classification

Keras
ecg
cardiovascular
multi-label-classification
ptb-xl
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hellothisisaswin Steenslid commited on
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Duplicate from Steenslid/ecg-ptbxl-classification

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Co-authored-by: Edvard Vindenes Steenslid <Steenslid@users.noreply.huggingface.co>

Files changed (6) hide show
  1. .gitattributes +38 -0
  2. README.md +45 -0
  3. ecg_cnn_final.keras +3 -0
  4. ecg_model.keras +3 -0
  5. normalisation_params.npz +3 -0
  6. thresholds.json +7 -0
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+ ecg_cnn_final.keras filter=lfs diff=lfs merge=lfs -text
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README.md ADDED
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+ ---
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+ tags:
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+ - ecg
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+ - cardiovascular
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+ - multi-label-classification
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+ - keras
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+ - ptb-xl
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+ datasets:
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+ - ptb-xl
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+ license: mit
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+ ---
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+
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+ # ECG Cardiovascular Disease Classification
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+
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+ Multi-label classification of 5 cardiovascular superclasses
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+ (NORM, MI, STTC, CD, HYP) from 12-lead ECG recordings, trained on PTB-XL.
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+
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+ **Deployed model**: CNN (noaug training variant)
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+
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+ ## Files
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+
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+ - `ecg_model.keras` | trained model
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+ - `normalisation_params.npz` | per-channel mean and std (z-score, from training fold)
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+ - `thresholds.json` | per-class decision thresholds optimised on the validation fold
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+
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+ ## Usage
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+
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+ ```python
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+ import keras, numpy as np, json
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+ from huggingface_hub import hf_hub_download
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+
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+ model = keras.saving.load_model(
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+ hf_hub_download("Steenslid/ecg-ptbxl-classification", "ecg_model.keras"))
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+ params = np.load(hf_hub_download("Steenslid/ecg-ptbxl-classification", "normalisation_params.npz"))
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+ with open(hf_hub_download("Steenslid/ecg-ptbxl-classification", "thresholds.json")) as f:
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+ thresholds = json.load(f)
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+
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+ # Input x: (1000, 12) float32 ECG in mV, 100 Hz, standard 12-lead order
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+ x_norm = (x - params["mean"]) / params["std"]
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+ probs = model.predict(x_norm[np.newaxis])[0]
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+ preds = {sc: probs[i] >= thresholds[sc] for i, sc in enumerate(
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+ ["NORM","MI","STTC","CD","HYP"])}
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+ ```
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+
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+ **Authors:** Edvard Vindenes Steenslid & Morten Kvamme
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+ {
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+ "NORM": 0.44000000000000006,
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+ "MI": 0.35000000000000003,
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+ "STTC": 0.33,
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+ "CD": 0.26000000000000006,
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+ "HYP": 0.2
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+ }