Densely connected convolutional networks for detection of atrial fibrillation from short single-lead ECG recordings.

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 s). For this purpose, we combined a signal quality index (SQI) algorithm, to assess noisy instances, and trained densely connected convolutional neural networks to classify ECG recordings. Two convolutional neural network (CNN) models (a main model that accepts 15 s ECG segments and a secondary model that processes shorter 9 s segments) 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. The results achieved on the 2017 PhysioNet/Computing in Cardiology challenge test dataset were an overall F1 score of 0.82 (F1 for NSR, AF, and O were 0.91, 0.83, and 0.72, respectively). Compared with 80 challenge entries, this was the third best overall score achieved on the evaluation dataset.