开源工具生成四种心电图数据集，支持信号识别与重叠波形分割。
An Open-Source Python Framework and Synthetic ECG Image Datasets for Digitization, Lead and Lead Name Detection, and Overlapping Signal Segmentation
- 用PTB-XL数据生成带时序信号的合成心电图图像
- 创建含边界框标注的检测数据集，支持导联与名称识别
- 首次实现相邻导联波形叠加但掩码保持清晰的分割数据
我们提出一个开源Python框架，用于生成合成心电图图像数据集，以推动基于深度学习的心电图分析任务，包括心电图数字化、导联区域与导联名称检测、像素级波形分割。基于PTB-XL信号数据集，该框架生成四个公开数据集：(1) 包含不同导联配置的图像与对应时序信号，用于心电图数字化；(2) 标注了YOLO格式边界框的图像，用于导联区域与导联名称检测；(3)-(4) 分别为正常与重叠版本的单导联裁剪图像，配有适配U-Net模型的分割掩码。在重叠情况下，相邻导联的波形被叠加到目标导联图像上，而分割掩码保持完整无误。开源框架与数据集已公开发布于https://github.com/rezakarbasi/ecg-image-and-signal-dataset及https://doi.org/10.5281/zenodo.15484519。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。