  The development of new technology such as wearables that record high-quality
single channel ECG, provides an opportunity for ECG screening in a larger
population, especially for atrial fibrillation screening. The main goal of this
study is to develop an automatic classification algorithm for normal sinus
rhythm (NSR), atrial fibrillation (AF), other rhythms (O), and noise from a
single channel short ECG segment (9-60 seconds). For this purpose, signal
quality index (SQI) along with dense convolutional neural networks was used.
Two convolutional neural network (CNN) models (main model that accepts 15
seconds ECG and secondary model that processes 9 seconds shorter ECG) were
trained using the training data set. If the recording is determined to be of
low quality by SQI, it is immediately classified as noisy. Otherwise, it is
transformed to a time-frequency representation and classified with the CNN as
NSR, AF, O, or noise. At the final step, a feature-based post-processing
algorithm classifies the rhythm as either NSR or O in case the CNN model's
discrimination between the two is indeterminate. The best result achieved at
the official phase of the PhysioNet/CinC challenge on the blind test set was
0.80 (F1 for NSR, AF, and O were 0.90, 0.80, and 0.70, respectively).
