A Novel Atrial Fibrillation Prediction Algorithm Applicable to Recordings from Portable Devices

Atrial Fibrillation (AFib) is by itself a strong risk factor for many life-threatening heart diseases. An estimated 2.7 to 6.1 million people in the United States have AFib. With the aging of the U.S. population, this number is expected to increase. In this preliminary study, a heart rate-duration criteria region is proposed to automatically label symptomatic AFib events using recordings from portable ECG monitors. A Markov Chain algorithm is implemented to classify prediction intervals that are 2 minutes before the symptomatic AFib events. The method yields an overall accuracy value of 82% with 0.91 AUC.