ArticleJournal of medical signals and sensors2026
SynthECG: Python Framework and ECG Image Datasets for Digitization, Lead Detection, and Waveform Segmentation.
Article in Journal of medical signals and sensors, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Digitizing electrocardiogram (ECG) images into structured time-series data is critical for clinical analysis, but it remains challenging due to the lack of standardized datasets, especially under realistic scenarios like overlapping waveforms. Methods: We introduce SynthECG, an open-source Python framework to generate four synthetic ECG datasets tailored for deep learning tasks, including ECG digitization, YOLO-based lead and lead name detection, and U-Net-based waveform segmentation. The framework supports customizable parameters (e.g., dataset size, lead layout, and visual style) and allows generating up to 21,799 images for multi-lead datasets and 261,588 for single-lead segmentation. Notably, it introduces a novel mechanism to simulate overlapping waveforms from adjacent leads while preserving clean segmentation masks. Results: Using our framework, we generated four open-access datasets: (1) 2000 ECG images in various lead configurations paired with time-series signals for ECG digitization, (2) 2000 ECG images in various lead configurations with YOLO-format annotations for detecting lead regions and lead names, (3) 20,000 cropped single-lead images with pixel-level segmentation masks (normal variant), and (4) 102 cropped single-lead images with overlapping waveforms from adjacent leads (overlapping variant). We validated these datasets through two case studies: digitization using a non-ML algorithm (mean squared error: 0.002, ρ: 0.93, signal-to-noise ratio [SNR]: 7.36 dB, SNR Conclusions: Our open-source framework enables the generation of large-scale, customizable ECG image datasets to support key deep learning-based tasks, including digitization under normal and overlapping conditions, as well as lead region and lead name detection. The full datasets and code are publicly available at: https://doi.org/10.5281/zenodo.15484519 and https://github.com/rezakarbasi/ecg-image-and-signal-dataset.
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