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

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.