Window-based energy-aware model for real-time detection and reporting of progressive development of cardiac 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. However they are not energy-aware and fall short on delivering real-time early detection and reporting of progressive development of cardiac atrial fibrillation (A-Fib). Previous work by the same authors proposes adopting an incidence-based energy-aware model that incorporates a real-time detection algorithm for the onset of A-Fib using an A-Fib incidence rate in a wearable computing device during a 24 hour period. The results of the adopted incidence-based energy-aware model show an improvement of 38.2% when compared to the energy consumed by current telemetry energy model. This paper extends the previous design to the paroxysmal phase of A-Fib within a personalized A-Fib prevalence window lasting up to 7 days in order to monitor and detect the progressive development of A-Fib in wearable computing devices. The results from the new window-based energy-aware model show that the proposed energy model may potentially consume 89.7% less energy than the telemetry energy model. The design shows promising results in further meeting the energy needs for real-time detection and reporting of progressive development of cardiac A-Fib in wearable computing devices.