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ecgxai

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software/ecgxai

Neatly packaged AI methods for explainable ECG analysis

Machine-generated from the listed sources and not yet reviewed by a human.

record
Category
Software & Systems
Subcategory
unknown
License
AGPL-3.0(osi)
Status
active
Maturity
deployed
Organization
UMCUtrecht-ECGxAI
Country
unknown
Homepage
unknown
Documentation
unknown
Tags
deep-learning · deep-neural-networks · ecg · explainable-ai · variational-autoencoder
Regulatory
unknown
built by · 2

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.

similar by tags

Computed from shared tags, weighted so a rare tag counts for more than a common one. These are suggestions, not curated relationships.

  • Autoencoders for Unsupervised Anomaly Segmentation in Brain MR Images: A Comparative Study

  • IMLE-Netecg · explainable-ai

    Official implementation of our IEEE:SMC 2021 paper "IMLE-Net: An Interpretable Multi-level Multi-channel Model for ECG Classification"

  • XAI_SignalCAMecg · explainable-ai

    PyTorch implementation of the lightweight CNN-based ECG classification approach with integrated explainable AI (XAI) capabilities presented in the paper "Lightweight Data-driven ECG Classification…

  • LDCNN: A new arrhythmia detection technique with ECG signals using a linear deep convolutional neural network

  • PhysioNet-CinC-Challenge2020-TeamUIOdeep-neural-networks · ecg

    Public repository associated with: Convolutional Neural Network and Rule-Based Algorithms for Classifying 12-lead ECGs

  • ResUNet-LCdeep-neural-networks · ecg

    2D residual U-Net (ResUNet) and a lead combiner (LC) for 12-lead ECG Abnormality Classification

sources
  1. api.github.com/repos/UMCUtrecht-ECGxAI/ecgxai
    retrieved 2026-08-05 · via github-api

    Machine-imported from GitHub search. Last push 2026-07-02, 101 stars, license reported as AGPL-3.0. Category and schematic were assigned by keyword heuristics and are unreviewed.

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machine-readable

/v1/entries/4.json→ .entries["ecgxai"]

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