Remote Monitoring of Cardiovascular Autonomic Dysfunction in Synucleinopathies With a Wearable Chest Patch

• Published in: IEEE sensors journal Vol. 25; no. 4; pp. 7250 - 7262
• Main Authors: Berkebile, John A., Gazi, Asim H., Chan, Michael, Albarran, Tyler D., Rozell, Christopher J., Inan, Omer T., Beach, Paul A.
• Format: Journal Article
• Language: English
• Published: New York IEEE 15.02.2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Ambulatory monitoring; Atmospheric measurements; Atrophy; Biomedical monitoring; Blood pressure; cardiovascular sensing; Diseases; electrocardiogram (ECG); Electrocardiography; Heart rate; Heart rate variability; Hypotension; Lewy body diseases (LBD); Machine learning; machine learning (ML); Motors; orthostatic hypotension (OH); Parkinson's disease; Parkinson’s disease (PD); Particle measurements; photoplethysmogram (PPG); physiological signals; Protocols; Remote monitoring; seismocardiogram (SCG); Syncope; Telemedicine; Testing; Waveforms; wearable sensing; Wearable technology
• ISSN: 1530-437X; 1558-1748
• DOI: 10.1109/JSEN.2024.3523849

In neurodegenerative conditions like Parkinson's disease (PD) and multiple system atrophy (MSA), cardiovascular autonomic dysfunction (CVAD) is associated with several poor long-term health outcomes. CVAD commonly manifests as orthostatic hypotension (OH), a sustained drop in blood pressure (BP) upon standing that can cause syncope and falls. Conventional screening methods for OH are suboptimal and formal autonomic testing is limited to specialized centers. This study explores a multimodal wearable sensing patch for remote monitoring of CVAD. We collected waveform data during clinical autonomic testing and a 24-h period at home from 20 participants with synucleinopathies (12 with OH) and six healthy controls. We developed an automated posture detection pipeline that identified 103 at-home orthostatic events. Then, physiomarkers related to heart rate variability (HRV), cardiac mechanics, and vasomotor function were derived during the supine and standing periods associated with clinical and at-home orthostatic transitions. Comparisons of baroreflex-related supine physiomarkers revealed significant differences between those with and without OH. We characterized cardiovascular autonomic dynamics while standing, leveraging low-dimensional representations, and found marked differences in the aggregate responses between groups. We also observed significantly higher within-subject similarity between the at-home responses of the OH group. Finally, we examined the discriminative power of the patch's physiomarkers and demonstrated accurate classification of persons with OH during the clinical stand testing (<inline-formula> <tex-math notation="LaTeX">{F} 1=0.83 </tex-math></inline-formula>). This study is the first to couple orthostatic event detection with machine learning (ML) analysis of wearable-derived physiomarkers, illustrating that wearable sensing can accurately classify OH and provide novel insights into CVAD outside the clinic.