A Mobile Health System to Identify the Onset of Paroxysmal Atrial Fibrillation

Atrial Fibrillation (AFib) is increasingly recognized as a risk factor for clots, strokes, heart failure and other complications. One estimate states that 2.7 million individuals are living in U.S. with AFib and this number may increase to 5.6 million by 2050. Identifying patients with paroxysmal AFib early after the onset and treating them immediately may improve clinical outcomes, especially by reducing stroke. Currently AFib cases are identified only when the patients complain of palpitations or discovered during routine heart check ups. Improving early identification warrants a simple screening device to detect the onset of AFib. We have developed an mHealth system with a wearable ECG and an automated algorithm for this purpose. The machine learning based algorithm along with patient user interface can be downloaded as an App.