An energy efficient model for monitoring and detecting atrial fibrillation in wearable computing

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 a 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.