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- W3012420014 abstract "Heart disease is a leading cause of death in the US. Recent advances in the Internet of Things (IoT) provide a great opportunity to realize smart and connected health systems through IoT monitoring and sensor-based data analytics of cardiac disorders. However, big data arising from the large-scale IoT system pose a significant challenge for efficient and effective sensory information processing and decision making. Very little has been done to glean pertinent information about the disease-altered cardiac activity in the context of large-scale IoT network. In this study, we propose a parallel computing framework for multi-level network modeling and monitoring of cardiac dynamics to realize the potential of IoT-enabled smart health management. Specifically, dissimilarities among cardiac signals are firstly characterized among heartbeats for an individual patient, as well as among representative heartbeats for different patients. Then, a stochastic learning approach is developed to optimize the embedding of cardiac signals into a beat-to-beat network model, as well as a patient-to-patient network model. Further, we develop a parallel computing algorithm to improve the computational efficiency. Finally, a statistical process monitoring scheme is designed to harness network features for real-time monitoring and anomaly detection of cardiac activities. Experimental results show the proposed methodology has strong potential to realize a smart and interconnected system for cardiac health management in the context of large-scale IoT network." @default.
- W3012420014 created "2020-03-23" @default.
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- W3012420014 date "2020-04-06" @default.
- W3012420014 modified "2023-10-15" @default.
- W3012420014 title "Network modeling and Internet of things for smart and connected health systems—a case study for smart heart health monitoring and management" @default.
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- W3012420014 doi "https://doi.org/10.1080/24725579.2020.1741738" @default.
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