Modeling cardiac/cerebrovascular variability from wearable biometrics

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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.