  Atrial Fibrillation (AF) is a common cardiac arrhythmia affecting a large
number of people around the world. If left undetected, it will develop into
chronic disability or even early mortality. However, patients who have this
problem can barely feel its presence, especially in its early stage. A
non-invasive, automatic, and effective detection method is therefore needed to
help early detection so that medical intervention can be implemented in time to
prevent its progression.
  Electrocardiogram (ECG), which records the electrical activities of the
heart, has been widely used for detecting the presence of AF. However, due to
the subtle patterns of AF, the performance of detection models have largely
depended on complicated data pre-processing and expertly engineered features.
In our work, we developed DenseECG, an end-to-end model based on 5 layers 1D
densely connected convolutional neural network. We trained our model using the
publicly available dataset from 2017 PhysioNet Computing in Cardiology(CinC)
Challenge containing 8528 single-lead ECG recordings of short-term heart
rhythms (9-61s). Our trained model was able to outperform the other
state-of-the-art AF detection models on this dataset without complicated data
pre-processing and expert-supervised feature engineering.
