VitalDB Arrhythmia Database: An Anesthesiologist-Validated Large-Scale Intraoperative Arrhythmia Dataset with Beat and Rhythm Labels

Intraoperative cardiac arrhythmias present distinct characteristics and clinical challenges compared to non-surgical environments, yet publicly available electrocardiogram (ECG) databases have primarily focused on ambulatory and intensive care environments. To address this gap, we present the VitalDB Arrhythmia Database, a comprehensive collection of annotated intraoperative ECG recordings specifically designed for developing and validating arrhythmia detection algorithms in the surgical context. The database comprises 734,528 seconds of continuous ECG data from 482 surgical patients, with over 660,000 individually annotated heartbeats classified 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 served 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. 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 detection algorithms suited to the unique physiological and technical challenges of the perioperative environment.