构建1.7万张带完整标注的合成心电图图像数据集，助力深度学习自动识别纸质心电图。
PTB-XL-Image-17K: A Large-Scale Synthetic ECG Image Dataset with Comprehensive Ground Truth for Deep Learning-Based Digitization
- 从真实信号生成高质量12导联心电图图像，支持多种纸速与电压参数配置
- 每张图像配齐像素级分割、波形边界框和真实时间序列信号，标注完备
- 开源生成框架可自定义参数，适合医学图像分析与心电图数字化研究者
心电图（ECG）数字化——将纸质或扫描的心电图图像还原为时间序列信号——对于在现代深度学习中利用数十年的临床遗留数据至关重要。然而，由于缺乏大规模同时提供心电图图像及其对应真实信号与全面标注的数据集，该领域进展受阻。本文提出PTB-XL-Image-17K，一个由PTB-XL信号数据库生成的大型合成心电图图像数据集，包含17,271张高质量12导联心电图图像。每个样本提供五种互补数据：（1）具有真实网格与标注的逼真图像（50%含网格，50%无网格），（2）像素级分割掩码，（3）真实时间序列信号，（4）以YOLO格式标注的导联区域与导联名称边界框，（5）包含视觉参数与患者信息的完整元数据。我们开发了一个开源Python框架，支持可控参数生成，包括纸速（25/50 mm/s）、电压刻度（5/10 mm/mV）、采样率（500 Hz）、网格颜色（4种）及波形特征。数据集生成成功率100%，平均每样本处理时间1.35秒。PTB-XL-Image-17K首次提供了覆盖完整流程的大规模资源：导联检测、波形分割与信号提取，具备完整真实标签，可用于严格评估。数据集、生成框架与文档已公开，地址为https://github.com/naqchoalimehdi/PTB-XL-Image-17K 和 https://doi.org/10.5281/zenodo.18197519。
原文摘要 · Abstract (English)
Electrocardiogram (ECG) digitization-converting paper-based or scanned ECG images back into time-series signals-is critical for leveraging decades of legacy clinical data in modern deep learning applications. However, progress has been hindered by the lack of large-scale datasets providing both ECG images and their corresponding ground truth signals with comprehensive annotations. We introduce PTB-XL-Image-17K, a complete synthetic ECG image dataset comprising 17,271 high-quality 12-lead ECG images generated from the PTB-XL signal database. Our dataset uniquely provides five complementary data types per sample: (1) realistic ECG images with authentic grid patterns and annotations (50% with visible grid, 50% without), (2) pixel-level segmentation masks, (3) ground truth time-series signals, (4) bounding box annotations in YOLO format for both lead regions and lead name labels, and (5) comprehensive metadata including visual parameters and patient information. We present an open-source Python framework enabling customizable dataset generation with controllable parameters including paper speed (25/50 mm/s), voltage scale (5/10 mm/mV), sampling rate (500 Hz), grid appearance (4 colors), and waveform characteristics. The dataset achieves 100% generation success rate with an average processing time of 1.35 seconds per sample. PTB-XL-Image-17K addresses critical gaps in ECG digitization research by providing the first large-scale resource supporting the complete pipeline: lead detection, waveform segmentation, and signal extraction with full ground truth for rigorous evaluation. The dataset, generation framework, and documentation are publicly available at https://github.com/naqchoalimehdi/PTB-XL-Image-17K and https://doi.org/10.5281/zenodo.18197519.
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