Preliminary Simulation for Obtaining Accurate Heart Rate Data Using Imputation Methods

Abstract

Increasingly data acquired from consumer activity trackers are being used by clinicians to monitor and treat patients. There are many reported cases where medical doctors leverage data retrieved from these devices to successfully treat and diagnose patients with heart conditions. In addition, hospitals are testing these devices in clinical trials to support telemedicine. One challenge is sensor susceptibility to distortions, resulting in missingness and impacting on validity. In this study we are exploring optimal methods of imputation through an experimental simulation using heart rate interval data. For this initial methodology we are utilizing Fitbit heart rate data to investigate performances of four imputation methods using 1000 Monte Carlo runs. Normalized errors from simulated missingness experiments were determined by comparing observed and predicted values. Preliminary findings show that linear and Kalman imputation methods are most appropriate when imputing heart rate data.