Abstract:
Atrial brillation (AF) is an abnormal heart rhythm that takes place
when electrical impulses re o from multiple places in the atria (the top
chambers of the heart) in a disorganized way. This causes the atria to twitch
and results in an irregular heartbeat or pulse. Atrial brillation is a major
cause of stroke. Atrial Fibrillation is usually screened manually with
the help of Electrocardiodiagram (ECG) reading. Manually reading ECG
is usually a tedious and time-consuming task, which is laden with human
errors. Therefore, an automated process is quintessential. However, discerning
anomaly in heart function using an e cient automated process has been
a challenging task for quite some time. In this paper we propose two intricate
Neural Network architectures for the classi cation amongst four types
of heart condition-Normal, Atrial Fibrillation, Noisy Sinus Rhythm and Alternative
Rhythm, using a dataset from PhysioNet/2017 challenge. Volunteers
in PhysioNet/2017 challenge dataset came from diverse background
and had a wide window of variation in their physical attributes, making
the dataset su ciently reliable. Also, the size of this dataset exceeded any
other before it on this topic, which further adds to the comprehensiveness
of this dataset.Initially, a preprocessing is done on the dataset to make it
more robust and push the accuracy to its edge. We then trained a Deep
Neural Network, which combined feature extraction layers of CNN with
long-short term memory (LSTM). Our method reached a summit accuracy
of 91.19%.The second model just applied a CNN model and the resulting
output reached an accuracy of 84.3%
