  Atrial fibrillation is increasingly prevalent, especially in the elderly, and
challenging to detect due paroxysmal nature. Here, we propose novel
computational methods based on heart beat intervals to facilitate rapid and
robust discrimination between atrial fibrillation and sinus rhythm. We used
low-cost Android smartphones, and recorded short, 30 second waveform data from
194 participants. In addition, we evaluated our approach on 8528 hand-held ECG
recordings to show generalization.
  Our approach achieves a sensitivity of 93% and specificity of 94% on 30
second waveforms, significantly outperforming previously proposed heart rate
variability features and smartphone-based AFib detection methods, and
substantiates the feasibility of real-world application on low-cost hardware.
