Modeling cardiac/cerebrovascular variability from wearable biometrics

通过可穿戴生物识别技术对心/脑血管变异进行建模

• 批准号: RGPIN-2020-06430
• 负责人: Butler, Russell
• 金额: $ 1.75万
• 依托单位: Bishop's University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=749785
• 关键词: Modeling; cardiac; cerebrovascular; variability; wearable

Modeling cardiac/cerebrovascular variability from wearable biometrics Mass, population-wide monitoring of biometrics such as heartrate variability (HRV) is now feasible using small, inexpensive wearables such as Apple Watch and Samsung Galaxy Watch. Smartwatches are capable of long-term HRV monitoring through optical sensing at the wrist, yielding a continuous and reliable measure of physiological state 24/7. The raw optical signal is a periodic function containing information about many HRV indices, including: resting heart rate, active heart rate, perfusion index, respiratory sinus arrhythmia, and circadian-linked heartrate dependencies. Despite the expanding consumer base and clear potential of these devices (50 million Apple Watches sold in 2018, with validated atrial fibrillation detection), the information contained in continuous optical smartwatch recordings remains largely untapped. We still lack a basic understanding of how inter-individual variability in cerebral/cardiac anatomy and function relates to HRV across the general population, and in particular, long term HRV measured using smartwatches. For example: if an individual exhibits irregular HRV during deep sleep, does it reflect an underlying problem in their cerebral/cardiac anatomy or function? Hence, the principal objective of this research is to construct a model linking smartwatch measures of HRV to cardiac and cerebrovascular measures obtained from magnetic resonance imaging (MRI). This will be accomplished by 1) performing long-term HRV monitoring using a commercial wearable device in a sample from the general population, 2) acquiring high resolution quantitative cardiac/cerebrovascular MRI measures in the same group of subjects, and 3) building a model to link the two datasets. By building a model capable of predicting an individual's MRI scans based on smartwatch-derived HRV, we will take the first step towards a truly comprehensive, mass-scale monitoring of the general population to predict illness and death. Many disease of the western world are highly preventable; it is estimated that 50% of cardiovascular mortality can be avoided with the complete elimination of risk factors, and 25% of all strokes are considered highly preventable. This project will investigate the extent to which smartwatch-derived HRV can predict cerebral and cardiac variability across a healthy population, demonstrating the feasibility of using smartwatches to predict more expensive measures such as MRI, and eventually, give early warning signs of cerebrovascular and cardiovascular incidents.

大规模、全民范围内的生物特征监测，如心率变异性（HRV），现在可以使用小型、廉价的可穿戴设备，如苹果手表和三星Galaxy手表。智能手表能够通过腕部的光学传感器长期监测心率，全天候提供连续可靠的生理状态测量。原始光信号是一个包含许多HRV指数信息的周期函数，包括：静息心率、活动心率、灌注指数、呼吸性窦性心律失常和与昼夜节律相关的心率依赖性。尽管这些设备的消费者基础不断扩大，潜力也很明显（2018年苹果手表销量为5000万，具有有效的房颤检测功能），但连续光学智能手表记录中包含的信息在很大程度上仍未得到开发。我们仍然缺乏对大脑/心脏解剖结构和功能的个体差异与普通人群HRV之间关系的基本理解，特别是使用智能手表测量的长期HRV。例如：如果一个人在深度睡眠中表现出不规则的心率波动，这是否反映了他们的大脑/心脏解剖结构或功能存在潜在问题？因此，本研究的主要目的是构建一个模型，将智能手表的心率测量值与磁共振成像（MRI）获得的心脑血管测量值联系起来。这将通过1)使用商业可穿戴设备在普通人群样本中进行长期HRV监测，2)在同一组受试者中获得高分辨率定量心/脑血管MRI测量，以及3)建立模型将两个数据集联系起来。通过建立一个能够基于智能手表衍生的心率变异预测个人核磁共振扫描的模型，我们将向真正全面、大规模地监测普通人群以预测疾病和死亡迈出第一步。西方世界的许多疾病是完全可以预防的；据估计，如果完全消除危险因素，50%的心血管死亡是可以避免的，25%的中风被认为是高度可预防的。该项目将研究智能手表衍生的心率变异性在多大程度上可以预测健康人群的大脑和心脏变异性，证明使用智能手表预测更昂贵的措施（如MRI）的可行性，并最终给出脑血管和心血管事件的早期预警信号。