ECG-Multi-Label-Classification
importeddata/ecg-multi-label-classification
Graph Neural Network (GNN) approach for multi-label classification of clinical ECG data using PyTorch Geometric, NeuroKit2, and the PTB-XL dataset.
Machine-generated from the listed sources and not yet reviewed by a human.
- Category
- Data & Standards
- Subcategory
- unknown
- License
- MIT(osi)
- Status
- active
- Maturity
- deployed
- Organization
- unknown
- Country
- unknown
- Homepage
- unknown
- Documentation
- unknown
- Tags
- deep-learning · ecg · electrocardiogram · graph-neural-networks · medical-ai · multi-label-classification · neurokit2 · optuna · ptb-xl · pytorch-geometric · time-series-analysis
- Regulatory
- unknown
Computed from shared tags, weighted so a rare tag counts for more than a common one. These are suggestions, not curated relationships.
- ECG-TransNetecg · multi-label-classification · ptb-xl
Official PyTorch implementation of "ECG TransNet: Intra/Inter-Lead Feature Integration with Proxy-Guided Learning for 12-Lead ECG Classification" (ICASSP 2026). A SOTA multi-label 12-lead ECG…
- SynthECGecg · electrocardiogram · medical-ai · ptb-xl
The first systematic evaluation framework for synthetic 10-second 12-lead ECGs from diagnostic class-conditioned generative models
- pocket-cfdmgraph-neural-networks · pytorch-geometric
Augmenting a training dataset of the generative diffusion model for molecular docking with artificial binding pockets
- ecg-qaecg · ptb-xl
Official repository for distributing ECG-QA dataset
- ecg-reasoning-benchmarkecg · ptb-xl
Official repository for distributing ECG-Reasoning-Benchmark dataset
- caves-datamulti-label-classification
CAVES-dataset accepted at SIGIR'22
- api.github.com/repos/VanshGupta18/ECG-Multi-Label-Classificationretrieved 2026-08-25 · via github-api
Machine-imported from GitHub search. Last push 2026-04-22, 5 stars, license reported as MIT. Category and schematic were assigned by keyword heuristics and are unreviewed.
Not yet verified by a human. Correct this record →
/v1/entries/0.json→ .entries["ecg-multi-label-classification"]
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