Frequent and unobtrusive monitoring of cardiovascular conditions with consumer electronics is a widely pursued goal, since it provides the most economic and effective way of preventing and managing cardiovascular diseases (CVDs) ─ the leading causes of death worldwide. However, most current wearable and flexible devices can only support the measurement of one or two types of vital signs, such as heart rate and blood oxygen level, due to the lack of physiological models to link the measured signals to cardiovascular conditions. Here, we report a stroke-volume allocation (SVA) model to quantify the cushioning function of arteries and empower nearly all existing cardiac sensors with new functions, including arterial stiffness evaluation, dynamic blood pressure tracking and classification of CVD-related heart damage. Large-scale clinical data testing involving a hybrid dataset taken from 6 hospitals/research institutes (9 open databases and 4 self-built databases from 878 subjects in total) and diverse measurement approaches was carried out to validate the SVA model. The results show that the SVA-based parameters correlate well with the gold-standard measurements in arterial stiffness and blood pressure and outperform the commonly used vital sign (e.g., blood pressure) alone in detecting abnormalities in cardiovascular systems.

利用消费电子产品对心血管状况进行频繁且不引人注目的监测是一个广泛追求的目标，因为它提供了预防和管理心血管疾病（CVD）（全世界主要死亡原因）的最经济、最有效的方法。然而，由于缺乏将测量信号与心血管状况联系起来的生理模型，目前大多数可穿戴和柔性设备只能支持测量一种或两种生命体征，例如心率和血氧水平。在这里，我们报告了一种每搏输出量分配（SVA）模型，用于量化动脉的缓冲功能，并为几乎所有现有的心脏传感器赋予新功能，包括动脉硬度评估、动态血压跟踪和CVD相关心脏损伤的分类。通过大规模临床数据测试，涉及来自 6 家医院/研究机构的混合数据集（9 个开放数据库和 4 个自建数据库，共 878 名受试者）和多种测量方法来验证 SVA 模型。结果表明，基于 SVA 的参数与动脉僵硬度和血压的金标准测量值密切相关，并且在检测心血管系统异常方面优于单独常用的生命体征（例如血压）。