AI-Driven Early Warning of Cardiovascular Abnormalities Using Continuous Wearable Biosensing

Abstract

Continuous wearable biosensing is shifting cardiovascular care from episodic hospital measurement toward longitudinal, ambient data collection, creating new possibilities for early detection of arrhythmias, ischemic patterns, and hemodynamic instability. However, the translation of this sensing capability into reliable early warning systems requires more than improved classification accuracy. It demands careful integration of sensing hardware, embedded computation, network infrastructure, learning pipelines, clinical workflows, and regulatory oversight. This paper presents a system-level analysis of artificial intelligence driven early warning architectures for cardiovascular abnormalities using wearable biosensors. It examines trade-offs between on-device and cloud inference, constraints on energy and data quality, the influence of motion artifacts and heterogeneous sensor modalities, and the organizational arrangements required for safe deployment. The discussion addresses robustness under dynamic ambulatory conditions, fairness across diverse populations, privacy-preserving learning approaches such as federated systems, and the governance mechanisms needed to maintain clinical trust. Rather than focusing on a single algorithm or sensor, the paper argues that sustainable early warning depends on a layered socio-technical infrastructure in which algorithmic performance is coupled with data stewardship, infrastructure resilience, and accountability. The analysis draws on current evidence from wearable photoplethysmography and electrocardiography studies, machine learning deployment research, and health data governance scholarship to articulate structural design principles for future cardiovascular biosensing systems.