<b>Resting ECG Segmentation Dataset</b>

<b>Resting ECG Segmentation Dataset (RDB)</b> is a fully annotated, 12-lead resting-state ECG corpus built for waveform‐level delineation research.<br><b>Origin</b> – RDB is a derivative of the large, open ChapmanECG arrhythmia database (Zheng et al., 2020).<b>Scope</b> – 2 399 recordings, each 10 s long, sampled at 500 Hz.<b>Annotations</b> – Every record is labelled for P-waves, QRS complexes and T-waves on <b>all 12 leads</b>. Initial marks were placed by trained physicians and subsequently reviewed and refined by senior cardiologists; each lead was annotated independently to preserve lead-specific morphology.<b>Rhythm diversity</b> – The set spans atrial fibrillation (AF), atrial flutter (AFL), atrial tachycardia (AT), supraventricular tachycardia (SVT) and multiple sinus irregularities, providing rich morphological variation for robust model training.The combination of high-quality beat-level labels and broad rhythm coverage makes RDB a strong benchmark for developing and evaluating ECG segmentation algorithms that must generalise across diverse clinical presentations.<br>Citations：Zheng W. et al., “A 12‑lead ECG Database for Arrhythmia Classification.” <i>Data in Brief</i> 24 (2020): 103838. DOI: 10.1016/j.dib.2019.103838.<br>Liu Y. et al., “Y‑Net‑ECG: A Multi‑Lead Informed and Interpretable Architecture for ECG Segmentation Across Diverse Rhythms.” <i>Expert Systems with Applications</i> (2025): 127955. DOI: https://doi.org/10.1016/j.eswa.2025.127955.