Anomaly Detection in Long-Term Wearable Cardiovascular Monitoring Using Uncertainty-Aware Deep Learning
Keywords:
cardiovascular monitoring; wearable sensors; deep learning; uncertainty quantification; anomaly detection; clinical decision support; edge computing; data governanceAbstract
Long-term wearable cardiovascular monitoring generates continuous streams of electrocardiographic and photoplethysmographic data that can support early detection of arrhythmias and other cardiac anomalies. Deep learning models have shown strong diagnostic performance in controlled settings, but deployment in ambulatory environments introduces significant uncertainty due to motion artifacts, sensor displacement, population heterogeneity, and distributional drift over time. This paper provides a system-level examination of anomaly detection in wearable cardiovascular monitoring using uncertainty-aware deep learning. It argues that predictive uncertainty should be treated as a systemic design requirement rather than a localized algorithmic property. The analysis covers architectural considerations, data governance, edge-cloud trade-offs, calibration, robustness, fairness, clinical accountability, sustainability, and policy implications. Uncertainty-aware mechanisms can reduce silent failures, support staged clinical escalation, and improve trust in longitudinal monitoring systems. However, these benefits depend on organizational integration, transparent data practices, and regulatory alignment. The paper synthesizes existing research on deep learning, uncertainty quantification, wearable health systems, and responsible clinical machine learning. It concludes that future systems must couple uncertainty estimation with operational workflows that can interpret and act on uncertain outputs. This requires continued collaboration among engineers, clinicians, regulators, and health system administrators to ensure that wearable cardiovascular monitoring becomes safe, equitable, and sustainable outside controlled research environments.
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