@article{berkebile.24,
	title={Remote monitoring of cardiovascular autonomic dysfunction in synucleinopathies with a wearable chest patch},
	author={Berkebile, J.A. and Gazi, A.H. and Chan, M. and Albarran, T.D. and Rozell, C.J. and Inan, O.T. and Beach, P.A.},
	year= 2025,
	month = jan,
    volume={25},
    number={4},
    pages={7250-7262},
	abstract = {In neurodegenerative conditions like Parkinson’s dis- ease (PD) and multiple system atrophy (MSA), cardiovascular au- tonomic dysfunction (CVAD) is associated with several poor long- term health outcomes. CVAD commonly manifests as orthostatic hypotension (OH), a sustained drop in blood pressure upon stand- ing that can cause syncope and falls. Conventional screening meth- ods 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 wave- form data during clinical autonomic testing and a 24-hour period at home from 20 participants with synucleinopathies (12 with OH) and 6 healthy controls. We developed an automated posture detection pipeline that identified 103 at-home orthostatic events. Then, physiomarkers related to heart rate variability, 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 (F1=0.83). This study is the first to couple orthostatic event detection with machine learning analysis of wearable-derived physiomarkers, illustrating that wearable sensing can accurately classify OH and provide novel insights into CVAD outside the clinic.},
	journal = {IEEE Sensors Journal},
	url = {https://ieeexplore.ieee.org/document/10834516},
	doi={10.1109/JSEN.2024.3523849}
}