  Atrial fibrillation(termed as AF/Afib henceforth) is a discrete and often
rapid heart rhythm that can lead to clots near the heart. We can detect Afib by
ECG signal by the absence of p and inconsistent intervals between R waves as
shown in fig(1). Existing methods revolve around CNN that are used to detect
afib but most of them work with 12 point lead ECG data where in our case the
health gauge watch deals with single-point ECG data. Twelve-point lead ECG data
is more accurate than a single point. Furthermore, the health gauge watch data
is much noisier. Implementing a model to detect Afib for the watch is a test of
how the CNN is changed/modified to work with real life data
