AI-Assisted Early Detection of Cardiovascular Abnormalities Using Continuous Wearable Sensor Signals
Keywords:
continuous wearable sensing; cardiovascular abnormality detection; artificial intelligence; system architecture; health data governance; clinical deploymentAbstract
The growing burden of cardiovascular disease has intensified interest in continuous monitoring paradigms that extend beyond episodic clinical encounters. Wearable devices now acquire high-frequency photoplethysmographic, electrocardiographic, accelerometric, and contextual signals over prolonged periods, creating opportunities for artificial intelligence systems to detect subtle anomalies before acute events. However, translating raw sensor streams into clinically trustworthy early warning requires careful consideration of sensing quality, system architecture, data governance, robustness, fairness, and deployment sustainability. This article presents a system-level examination of AI-assisted early detection of cardiovascular abnormalities using continuous wearable sensor signals. It discusses how motion artifact, signal degradation, and interindividual variability shape sensing infrastructure. The paper then analyzes architectural choices spanning on-device processing, edge aggregation, cloud inference, and adaptive model updating. It further addresses privacy-preserving learning, algorithmic bias, uncertainty quantification, regulatory validation, interoperability, and lifecycle sustainability. Rather than focusing on a single algorithm, the analysis emphasizes structural trade-offs that determine whether such systems can operate safely in real-world health ecosystems. The article argues that early detection platforms should be designed as socio-technical infrastructures with transparent governance, prospective clinical evidence, and mechanisms for continuous monitoring of model performance. These considerations are essential for moving beyond diagnostic accuracy toward equitable, robust, and sustainable cardiovascular care.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
