AI-Driven Detection of Cardiovascular Abnormalities from Wearable Sensor Data
Keywords:
artificial intelligence; cardiovascular abnormalities; wearable sensors; digital health; machine learning; health systemsAbstract
Cardiovascular diseases remain a leading cause of mortality, and wearable sensors offer continuous physiological streams that can transform detection, monitoring, and risk stratification. This paper presents an interdisciplinary systems analysis of AI-driven detection of cardiovascular abnormalities from wearable sensor data. It examines the clinical rationale, data characteristics, model architectures, and system-level trade-offs that define contemporary implementations. Rather than focusing solely on algorithmic accuracy, the analysis emphasizes infrastructure, deployment, robustness, fairness, governance, and sustainability. The discussion integrates perspectives from biomedical signal processing, distributed systems, machine learning, and health policy. Wearable data exhibit heterogeneity, motion sensitivity, and contextual noise, requiring substantial preprocessing and uncertainty management. Deep learning architectures can learn morphological and temporal patterns, but their clinical utility depends on integration with real-time infrastructure, interoperability, and rigorous prospective evaluation. Structural trade-offs include edge versus cloud processing, model complexity versus explainability, and personalization versus generalizability. Federated learning and decentralized architectures offer privacy-preserving routes for multi-institutional training. At the same time, algorithmic bias, ethical oversight, and governance remain critical concerns. The paper argues that sustainable implementations require lifecycle planning, energy-aware design, regulatory alignment, and equitable access. Forward-looking perspectives address multimodal fusion, adaptive calibration, and policy incentives for responsible deployment. Overall, the analysis frames AI-driven wearable cardiovascular detection as a socio-technical system in which reliable clinical benefit emerges from careful alignment among sensing, learning, infrastructure, and governance.
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