  Atrial Fibrillation is a common form of irregular heart rhythm that can be
very dangerous. Our primary goal is to analyze Atrial Fibrillation data within
ECGs to develop a model based only on RR-Intervals, or the length between
heart-beats, to create a real time classification model for Atrial Fibrillation
to be implemented in common heart-rate monitors on the market today.
Physionet's MIT-BIH Atrial Fibrillation Database
\cite{goldberger2000physiobank} and 2017 Challenge Database
\cite{clifford2017af} were used to identify patterns of Atrial Fibrillation and
test classification models on. These two datasets are very different. The
MIT-BIH database contains long samples taken with a medical grade device, which
is not useful for simulating a consumer device, but is useful for Atrial
Fibrillation pattern detection. The 2017 Challenge database includes short
($<60sec$) samples taken with a portable device and reveals many of the
challenges of Atrial Fibrillation classification in a real-time device. We
developed multiple SVM models with three sets of extracted features as
predictor variables which gave us moderately high accuracies with low
computational intensity. With robust filtering techniques already applied in
many Photoplethysmograph-based consumer heart-rate monitors, this method can be
used to develop a reliable real time model for Atrial Fibrillation detection in
consumer-grade heart-rate monitors.
