7th International Conference on Body Area Networks

Research Article

An Energy Efficient Model for Monitoring and Detecting Atrial Fibrillation in Wearable Computing

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  • @INPROCEEDINGS{10.4108/icst.bodynets.2012.249939,
        author={Redjem Bouhenguel and Imad Mahgoub and Mohammad Ilyas},
        title={An Energy Efficient Model for Monitoring and Detecting Atrial Fibrillation in Wearable Computing},
        proceedings={7th International Conference on Body Area Networks},
        publisher={ICST},
        proceedings_a={BODYNETS},
        year={2012},
        month={11},
        keywords={energy-aware wearable computing real-time monitoring sensing detection of cardiac atrial fibrillation logistic regression model of atrial fibrillation},
        doi={10.4108/icst.bodynets.2012.249939}
    }
    
  • Redjem Bouhenguel
    Imad Mahgoub
    Mohammad Ilyas
    Year: 2012
    An Energy Efficient Model for Monitoring and Detecting Atrial Fibrillation in Wearable Computing
    BODYNETS
    ICST
    DOI: 10.4108/icst.bodynets.2012.249939
Redjem Bouhenguel1, Imad Mahgoub1, Mohammad Ilyas1,*
  • 1: FAU
*Contact email: ilyas@fau.edu

Abstract

Current portable healthcare monitoring systems are small, battery-operated electrocardiograph devices that are used to record the heart’s rhythm and activity. These on-body healthcare devices fall short on delivering real-time continuous monitoring of early detection of cardiac atrial fibrillation (A-Fib) when the symptoms last only a short period of time and require a long battery life. The focus of this paper is the design of an energy efficient model for real-time early detection of A-Fib in a wearable computing device. The design is realized by incorporating an A-Fib risk factor and a real-time A-Fib incidence-based detection algorithm. The results of the design show that the proposed energy efficient model performs better than the telemetry energy model. The design shows promising results in meeting the energy needs of real-time monitoring, detecting and reporting required in wearable computing healthcare applications.