Capturing Resting Cardiovascular Coupling as an Indicator of Orthostatic Hypotension using a Multimodal Chest-Worn Patch

Vikram Abbaraju, John A. Berkebile, Paul A. Beach, Omer T. Inan

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Keywords: cardiovascular coupling, baroreflex sensitivity, multimodal wearable, orthostatic hypotension, neurodegenerative disease

Abstract: Orthostatic hypotension (OH), caused by efferent baroreflex failure, can lead to syncope and is associated with high mortality rates among individuals with neurodegenerative diseases. Several studies in recent years have aimed to estimate baroreflex sensitivity (BRS) during orthostatic stressors using measures of cardiovascular coupling (CVC): the degree of synchronization between time series cardiovascular signals. However, these efforts have relied on blood pressure sensing using bulky, wired setups, and the majority have only quantified changes in CVC during or after the occurrence of OH. In this study, we characterized CVC at rest in N = 26 participants (20 with a neurodegenerative disease) using a chest-worn patch that recorded electrocardiogram (ECG) and photoplethysmogram (PPG) signals. From the ECG and PPG data recorded during a 5-minute supine rest period prior to an orthostatic challenge, we derived interbeat interval (IBI) and PPG amplitude (PPGamp) time series features as indices of cardiac rhythm and vascular function, respectively. We then quantified the coupling between IBI and PPGamp using time delay stability (TDS). Following an active standing test, 12 participants experienced OH. We found that mean TDS during the rest period was 22.9% lower in the OH group than in the no-OH group (p < 0.01). Furthermore, we found that resting TDS was moderately correlated with the change in systolic blood pressure from supine to standing (ρ = 0.43, p < 0.05). Thus, we demonstrated the effectiveness of a multimodal wearable in capturing a marker of impaired resting CVC prior to OH occurrence. This work enables the deployment of wearable sensing for estimating BRS to assist with early screening of autonomic dysfunction in the future.

Track: 1. Digital Health Solutions (i.e. sensors and algorithms) for diagnosis, progress, and self-management

NominateReviewer: Omer T. Inan ([e-mail])

Submission Number: 41