  Electrocardiogram recognition of cardiac arrhythmias is critical for cardiac
abnormality diagnosis. Because of their strong prediction characteristics,
artificial neural networks are the preferred method in medical diagnosis
systems. This study presents a method to detect atrial fibrillation with lead-I
ECGs using artificial intelligence. The aim of the study is to compare the
accuracy of the diagnoses estimated by cardiologists and artificial
intelligence over lead-I ECGs using 12-lead ECGs as references. To evaluate the
performance of the proposed model, dataset were collected from China
Physiological Signal Challenge 2018. In the study, diagnoses were examined in
three groups as normal sinus rhythm, atrial fibrillation and OTHER. All rhythm
and beat types except NSR and AFIB were labeled as OTHER super-class. OTHER
contains First-degree atrioventricular blocks, Conduction disturbances, Left
bundle branch block, Right bundle branch block, Premature atrial contraction,
Premature ventricular contraction, ST-segment depression and ST-segment
elevated type ECGs. CurAlive A.I. model which is using DenseNet as a CNN
architecture and continuous wavelet transform as feature extraction method,
showed a great performance on classifying ECGs from only lead-I compared to
cardiologists. The AI model reached the weighted average precision, recall,
F1-score and total accuracy 94.1%, 93.6%, 93.7% and 93.6% respectively, and the
average of each of the three cardiologists has reached weighted average
precision, recall, F1-score and total accuracy 82.2%, 54.6%, 57.5% and 54.6%
respectively. This study showed that the proposed CNN model CurAlive, can be
used to accurately diagnose AFIB, NSR, and OTHER rhythm using lead-I ECGs to
accelerate the early detection of AFIB as a cardiologist assistant. It is also
able to identify patients into different risk groups as part of remote patient
monitoring systems.
