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.