Abstract

Cardiovascular diseases are the number one killer in the world, and a big contribution to that death may be attributed to arrhythmias, tachycardia, and heart failure. Traditional methods of monitoring, such as Holter monitors and ECGs in a clinical setting, provide limited continuous data. Wearable devices combined with Edge AI and cloud computing provide a breakthrough in real-time cardiovascular monitoring with opportunities for early detection and intervention.  This review appraises the role of Edge AI in wearable devices for real-time cardiovascular monitoring with a focus on arrhythmia detection, heart failure prediction, and tachycardia management, as well as considering the contribution of cloud-connected systems in predictive analytics and personal treatment. A systematic search was conducted in Google Scholar, PubMed, and Web of Science with Boolean search strings, targeting studies between 2015 and 2025. After screening 937 articles, 34 were selected based on their relevance and quality.  Edge AI models such as CNNs and RNNs achieved diagnostic accuracy of 85-98% for cardiovascular anomalies. Federated learning addressed privacy issues, while with cloud integration real-time processing and personalized treatment were made possible. Wearable devices with Edge AI allow efficient real-time monitoring of cardiovascular health, although data privacy, sensor accuracy, and model optimization remain open challenges. 

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