An Open-Source Python Framework and Synthetic ECG Image Datasets for Digitization, Lead and Lead Name Detection, and Overlapping Signal Segmentation

• URL: http://arxiv.org/abs/2506.06315v1
• Date: Mon, 26 May 2025 20:06:50 GMT
• Title: An Open-Source Python Framework and Synthetic ECG Image Datasets for Digitization, Lead and Lead Name Detection, and Overlapping Signal Segmentation
• Authors: Masoud Rahimi, Reza Karbasi, Abdol-Hossein Vahabie,
• Abstract summary: We introduce an open-source Python framework for generating synthetic ECG image datasets.<n>We produce four open-access datasets: ECG images in various lead configurations paired with time-series signals for ECG digitization, ECG images annotated with YOLO-format bounding boxes for detection of lead region and lead name, and single-lead images with segmentation masks compatible with U-Net-based models.
• Score: 0.24578723416255746
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce an open-source Python framework for generating synthetic ECG image datasets to advance critical deep learning-based tasks in ECG analysis, including ECG digitization, lead region and lead name detection, and pixel-level waveform segmentation. Using the PTB-XL signal dataset, our proposed framework produces four open-access datasets: (1) ECG images in various lead configurations paired with time-series signals for ECG digitization, (2) ECG images annotated with YOLO-format bounding boxes for detection of lead region and lead name, (3)-(4) cropped single-lead images with segmentation masks compatible with U-Net-based models in normal and overlapping versions. In the overlapping case, waveforms from neighboring leads are superimposed onto the target lead image, while the segmentation masks remain clean. The open-source Python framework and datasets are publicly available at https://github.com/rezakarbasi/ecg-image-and-signal-dataset and https://doi.org/10.5281/zenodo.15484519, respectively.

Related papers

• PTB-XL-Image-17K: A Large-Scale Synthetic ECG Image Dataset with Comprehensive Ground Truth for Deep Learning-Based Digitization [0.0]
  We introduce PTB-XL-Image-17K, a complete synthetic ECG image dataset comprising 17,271 high-quality 12-lead ECG images.<n>The dataset achieves 100% generation success rate with an average processing time of 1.35 seconds per sample.
  arXiv  Detail & Related papers  (2026-02-07T08:52:38Z)
• A Deep Learning Pipeline Using Synthetic Data to Improve Interpretation of Paper ECG Images [8.559073054541754]
  Cardiovascular diseases (CVDs) are the leading global cause of death, and early detection is essential to improve patient outcomes.<n>We propose a deep learning framework designed specifically to classify paper-like ECG images into five main diagnostic categories.<n>Our method was the winning entry to the 2024 British Heart Foundation Open Data Science Challenge.
  arXiv  Detail & Related papers  (2025-07-29T16:16:17Z)
• Learning General Representation of 12-Lead Electrocardiogram with a Joint-Embedding Predictive Architecture [0.0]
  We introduce ECG-JEPA, a self-supervised learning model for 12-lead ECG analysis.<n>It learns semantic representations of ECG data by predicting in the hidden latent space.<n> ECG-JEPA achieves state-of-the-art performance in various downstream tasks including ECG classification and feature prediction.
  arXiv  Detail & Related papers  (2024-10-11T06:30:48Z)
• ECG-Image-Database: A Dataset of ECG Images with Real-World Imaging and Scanning Artifacts; A Foundation for Computerized ECG Image Digitization and Analysis [4.263536786122581]
  ECG-Image-Database is a large and diverse collection of electrocardiogram (ECG) images generated from ECG time-series data. We used ECG-Image-Kit, an open-source Python toolkit, to generate realistic images of 12-lead ECG printouts from raw ECG time-series. The resulting dataset includes 35,595 software-labeled ECG images with a wide range of imaging artifacts and distortions.
  arXiv  Detail & Related papers  (2024-09-25T04:30:19Z)
• GKGNet: Group K-Nearest Neighbor based Graph Convolutional Network for Multi-Label Image Recognition [37.02054260449195]
  Multi-Label Image Recognition (MLIR) is a challenging task that aims to predict multiple object labels in a single image. We present the first fully graph convolutional model, Group K-nearest neighbor based Graph convolutional Network (GKGNet) Our experiments demonstrate that GKGNet achieves state-of-the-art performance with significantly lower computational costs.
  arXiv  Detail & Related papers  (2023-08-28T07:50:04Z)
• ECG-Image-Kit: A Synthetic Image Generation Toolbox to Facilitate Deep Learning-Based Electrocardiogram Digitization [3.4579920352329787]
  We introduce ECG-Image-Kit, an open-source toolbox for generating synthetic multi-lead ECG images with realistic artifacts from time-series data. As a case study, we used ECG-Image-Kit to create a dataset of 21,801 ECG images from the PhysioNet QT database. We trained a combination of a traditional computer vision and deep neural network model on this dataset to convert synthetic images into time-series data.
  arXiv  Detail & Related papers  (2023-07-04T22:42:55Z)
• CorrMatch: Label Propagation via Correlation Matching for Semi-Supervised Semantic Segmentation [73.89509052503222]
  This paper presents a simple but performant semi-supervised semantic segmentation approach, called CorrMatch. We observe that the correlation maps not only enable clustering pixels of the same category easily but also contain good shape information. We propose to conduct pixel propagation by modeling the pairwise similarities of pixels to spread the high-confidence pixels and dig out more. Then, we perform region propagation to enhance the pseudo labels with accurate class-agnostic masks extracted from the correlation maps.
  arXiv  Detail & Related papers  (2023-06-07T10:02:29Z)
• Distilling Ensemble of Explanations for Weakly-Supervised Pre-Training of Image Segmentation Models [54.49581189337848]
  We propose a method to enable the end-to-end pre-training for image segmentation models based on classification datasets. The proposed method leverages a weighted segmentation learning procedure to pre-train the segmentation network en masse. Experiment results show that, with ImageNet accompanied by PSSL as the source dataset, the proposed end-to-end pre-training strategy successfully boosts the performance of various segmentation models.
  arXiv  Detail & Related papers  (2022-07-04T13:02:32Z)
• Two-Stream Graph Convolutional Network for Intra-oral Scanner Image Segmentation [133.02190910009384]
  We propose a two-stream graph convolutional network (i.e., TSGCN) to handle inter-view confusion between different raw attributes. Our TSGCN significantly outperforms state-of-the-art methods in 3D tooth (surface) segmentation.
  arXiv  Detail & Related papers  (2022-04-19T10:41:09Z)
• PSGR: Pixel-wise Sparse Graph Reasoning for COVID-19 Pneumonia Segmentation in CT Images [83.26057031236965]
  We propose a pixel-wise sparse graph reasoning (PSGR) module to enhance the modeling of long-range dependencies for COVID-19 infected region segmentation in CT images. The PSGR module avoids imprecise pixel-to-node projections and preserves the inherent information of each pixel for global reasoning. The solution has been evaluated against four widely-used segmentation models on three public datasets.
  arXiv  Detail & Related papers  (2021-08-09T04:58:23Z)
• Semantic Segmentation with Generative Models: Semi-Supervised Learning and Strong Out-of-Domain Generalization [112.68171734288237]
  We propose a novel framework for discriminative pixel-level tasks using a generative model of both images and labels. We learn a generative adversarial network that captures the joint image-label distribution and is trained efficiently using a large set of unlabeled images. We demonstrate strong in-domain performance compared to several baselines, and are the first to showcase extreme out-of-domain generalization.
  arXiv  Detail & Related papers  (2021-04-12T21:41:25Z)
• Learning Spatial Context with Graph Neural Network for Multi-Person Pose Grouping [71.59494156155309]
  Bottom-up approaches for image-based multi-person pose estimation consist of two stages: keypoint detection and grouping. In this work, we formulate the grouping task as a graph partitioning problem, where we learn the affinity matrix with a Graph Neural Network (GNN) The learned geometry-based affinity is further fused with appearance-based affinity to achieve robust keypoint association.
  arXiv  Detail & Related papers  (2021-04-06T09:21:14Z)
• SE-ECGNet: A Multi-scale Deep Residual Network with Squeeze-and-Excitation Module for ECG Signal Classification [6.124438924401066]
  We develop a multi-scale deep residual network for the ECG signal classification task. We are the first to propose to treat the multi-lead signal as a 2-dimensional matrix. Our proposed model achieves 99.2% F1-score in the MIT-BIH dataset and 89.4% F1-score in Alibaba dataset.
  arXiv  Detail & Related papers  (2020-12-10T08:37:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.