Signal Quality for RR Interval Prediction on Wearable Sensors

Laura Gonzalez, Thomas Paniagua, Nathan Starliper, Edgar J. Lobaton

Abstract: Physiological responses are essential for health monitoring. Wearable devices are providing greater populations of people with the ability to monitor physiological signals during their day to day activities. However, wearable devices are particularly susceptible to degradation of signal quality due to noise from motion artifacts, environment, and user error. In this paper, we compare the impact of including signal quality on predictive models for RR intervals in a real world setting.