<p>Intraoperative cardiac arrhythmias present distinct characteristics compared to non-surgical environments, yet publicly available electrocardiogram (ECG) databases have primarily focused on ambulatory or intensive care environments. To address this gap, we present the VitalDB Arrhythmia Database, a comprehensive collection of intraoperative ECG recordings with beat and rhythm labels specifically designed for developing and validating arrhythmia detection algorithms in surgical patients. The database comprises 734,528 seconds of continuous ECG data from 482 surgical patients, with a median annotated recording duration of 20 minutes. It contains over 660,000 annotated heartbeats across four beat types and 10 distinct rhythm categories. To efficiently process the extensive source data, we developed a custom deep learning beat classifier that serves as an automated screening tool for arrhythmia candidate segments. All annotations underwent rigorous validation by five anesthesiologists, with each segment independently reviewed by at least two anesthesiologists, and 9.3% required full committee consensus. Inter-rater reliability analysis demonstrated excellent agreement with an overall Cohen’s kappa of 0.930 ± 0.130. This publicly accessible resource provides the research community with clinically validated intraoperative arrhythmia data, facilitating the development of robust arrhythmia detection algorithms and enabling multimodal analysis to investigate the hemodynamic impact of intraoperative arrhythmias.</p>