ECG-Representation-Learning
importedsoftware/ecg-representation-learning
Self-supervised pre-training for ECG representation with inspiration from transformers & computer vision
Machine-generated from the listed sources and not yet reviewed by a human.
- Category
- Software & Systems
- Subcategory
- unknown
- License
- MIT(osi)
- Status
- maintained
- Maturity
- deployed
- Organization
- unknown
- Country
- unknown
- Homepage
- unknown
- Documentation
- unknown
- Tags
- 12-lead-ecg · attention · bert · clustering · dino · ecg · nlp · pre-training
- Regulatory
- unknown
Top contributors by commit count, from the project’s public repository. Avatars are served by their origin, not stored here. To be removed from this list, open an issue.
Computed from shared tags, weighted so a rare tag counts for more than a common one. These are suggestions, not curated relationships.
- ECG-Multi-Label-Classification-Using-Multi-Model12-lead-ecg · ecg
In this project, we will perform 12-lead ECG Multi-label Classification. Specifically, we will design a multi-model utilizing the characteristics of diagnoses from the Shaoxing and Ningbo databases.
- medkitbert · nlp
Toolkit for a learning health system
- Feline-Projectbert · nlp
Domain-adaptive NLP pipeline for feline veterinary NER using BERT
- AttnSleepattention
[TNSRE 2021] "An Attention-based Deep Learning Approach for Sleep Stage Classification with Single-Channel EEG"
- CTNetattention
CTNet: A Convolutional Transformer Network for EEG-Based Motor Imagery Classification
- EEG-ATCNetattention
Attention temporal convolutional network for EEG-based motor imagery classification
- api.github.com/repos/StefanHeng/ECG-Representation-Learningretrieved 2026-08-05 · via github-api
Machine-imported from GitHub search. Last push 2025-09-17, 29 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/21.json→ .entries["ecg-representation-learning"]
Entries are sharded 64 ways by a stable hash of the id, so a consumer can find any record without an index.