AI R&D Team, Samsung R&D Institute Brazil (SRBR), Campinas, São Paulo, 13097-160, Brazil.
Health H/W R&D Group, Samsung Electronics Co Ltd, Suwon, 497335, South Korea.
Sci Data. 2024 Nov 15;11(1):1233. doi: 10.1038/s41597-024-04041-1.
Blood pressure (BP) is one of the most prominent indicators of potential cardiovascular disorders. Traditionally, BP measurement relies on inflatable cuffs, which is inconvenient and limit the acquisition of such important health-related information in general population. Based on large amounts of well-collected and annotated data, deep-learning approaches present a generalization potential that arose as an alternative to enable more pervasive approaches. However, most existing work in this area currently uses datasets with limitations, such as lack of subject identification and severe data imbalance that can result in data leakage and algorithm bias. Thus, to offer a more properly curated source of information, we propose a derivative dataset composed of 380 hours of the most common biomedical signals, including arterial blood pressure, photoplethysmography, and electrocardiogram for 1,524 anonymized subjects, each having 30 segments of 30 seconds of those signals. We also validated the proposed dataset through experiments using state-of-the-art deep-learning methods, as we highlight the importance of standardized benchmarks for calibration-free blood pressure estimation scenarios.
血压（BP）是潜在心血管疾病的最重要指标之一。传统上，BP 测量依赖于可充气袖带，这既不方便，又限制了一般人群中此类重要健康相关信息的获取。基于大量收集和注释的数据，深度学习方法提供了一种替代方法的泛化潜力，以实现更普遍的方法。然而，目前该领域的大多数现有工作都使用存在局限性的数据集，例如缺乏主体识别和严重的数据不平衡，这可能导致数据泄露和算法偏差。因此，为了提供更适当的信息来源，我们提出了一个衍生数据集，该数据集由最常见的生物医学信号组成，包括动脉血压、光体积描记法和心电图，涵盖 1524 名匿名受试者，每个受试者有 30 个 30 秒长的信号段。我们还通过使用最先进的深度学习方法的实验验证了所提出的数据集，因为我们强调了标准化基准对于无校准血压估计场景的重要性。
