VitalDB, a high-fidelity multi-parameter vital signs database in surgical patients
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In modern anesthesia, multiple medical devices are used simultaneously to comprehensively monitor real-time vital signs to optimize patient care and improve surgical outcomes. However, interpreting the dynamic changes of time- series biosignals and their correlations is a difficult task even for experienced anesthesiologists. Recent advanced machine learning technologies have shown promising results in biosignal analysis, however, research and development in this area is relatively slow due to the lack of biosignal datasets for machine learning. The VitalDB (Vital Signs DataBase) is an open dataset created specifically to facilitate machine learning studies related to monitoring vital signs in surgical patients. This dataset contains high- resolution multi-parameter data from 6,388 cases, including 486,451 waveform and numeric data tracks of 196 intraoperative monitoring parameters, 73 perioperative clinical parameters, and 34 time-series laboratory result parameters. All data is stored in the public cloud after anonymization. The dataset can be freely accessed and analysed using application programming interfaces and Python library. The VitalDB public dataset is expected to be a valuable resource for biosignal research and development.
在现代麻醉学领域，临床通常同时使用多台医疗设备，对患者的实时生命体征进行全面监测，以优化患者护理质量并提升手术预后效果。然而，即便对于经验丰富的麻醉医师而言，解读时序生物信号的动态变化及其相互关联仍是一项颇具挑战的工作。近年来，先进的机器学习技术在生物信号分析领域已展现出颇具潜力的应用成果，但由于缺乏适用于机器学习研究的生物信号数据集，该领域的研发进展相对缓慢。VitalDB（生命体征数据库，Vital Signs DataBase）是专为推动手术患者生命体征监测相关机器学习研究而构建的开源数据集。该数据集包含来自6388例病例的高分辨率多参数数据，涵盖196项术中监测参数、73项围手术期临床参数以及34项时序实验室检测参数，共计486451条波形与数值数据轨迹。所有数据均已完成匿名化处理后存储于公共云端。用户可通过应用程序编程接口（Application Programming Interface，API）与Python库自由访问并分析该数据集。该VitalDB开源数据集有望成为生物信号研究与开发领域的宝贵资源。
