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Feature/ludb ecg delineation #1015

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41ff181
ECG delineation task (Park et al. CHIL 2025)
Apr 6, 2026
8284a60
Merge branch 'sunlabuiuc:master' into feature/ludb-ecg-delineation
Gawainus Apr 19, 2026
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1 change: 1 addition & 0 deletions docs/api/datasets.rst
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Expand Up @@ -226,6 +226,7 @@ Available Datasets
datasets/pyhealth.datasets.MIMIC4Dataset
datasets/pyhealth.datasets.MedicalTranscriptionsDataset
datasets/pyhealth.datasets.CardiologyDataset
datasets/pyhealth.datasets.LUDBDataset
datasets/pyhealth.datasets.eICUDataset
datasets/pyhealth.datasets.ISRUCDataset
datasets/pyhealth.datasets.MIMICExtractDataset
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11 changes: 11 additions & 0 deletions docs/api/datasets/pyhealth.datasets.LUDBDataset.rst
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pyhealth.datasets.LUDBDataset
=============================

The Lobachevsky University Database (LUDB) contains 200 12-lead ECG recordings at 500 Hz from healthy volunteers and patients with cardiovascular diseases, with manual cardiologist annotations of P wave, QRS complex, and T wave boundaries. Refer to the `dataset page <https://physionet.org/content/ludb/1.0.1/>`_ for more information.

.. autoclass:: pyhealth.datasets.LUDBDataset
:members:
:undoc-members:
:show-inheritance:

.. autofunction:: pyhealth.datasets.get_stratified_ludb_split
1 change: 1 addition & 0 deletions docs/api/tasks.rst
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Expand Up @@ -209,6 +209,7 @@ Available Tasks
In-Hospital Mortality (MIMIC-IV) <tasks/pyhealth.tasks.InHospitalMortalityMIMIC4>
MIMIC-III ICD-9 Coding <tasks/pyhealth.tasks.MIMIC3ICD9Coding>
Cardiology Detection <tasks/pyhealth.tasks.cardiology_detect>
ECG Delineation (LUDB) <tasks/pyhealth.tasks.ecg_delineation>
COVID-19 CXR Classification <tasks/pyhealth.tasks.COVID19CXRClassification>
DKA Prediction (MIMIC-IV) <tasks/pyhealth.tasks.dka>
Drug Recommendation <tasks/pyhealth.tasks.drug_recommendation>
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6 changes: 6 additions & 0 deletions docs/api/tasks/pyhealth.tasks.ecg_delineation.rst
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pyhealth.tasks.ecg_delineation
===============================

ECG wave delineation task for the LUDB dataset. Segments each 10-second ECG lead signal into background (0), P wave (1), QRS complex (2), and T wave (3) regions. Designed for replication of Park et al., "Benchmarking ECG Delineation using Deep Neural Network-based Semantic Segmentation Models," CHIL 2025.

.. autofunction:: pyhealth.tasks.ecg_delineation_ludb_fn
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