Automatic varied-length ECG classification using a lightweight DenseNet model

Atrial fibrillation is the most common abnormal heart condition and contributes primarily to cardiac morbidity and mortality. In the last decades, portable Electrocardiogram (ECG) monitor has effectively supported patients with periodic heart arrhythmia by constantly monitoring heart activity and early detecting heart arrhythmia. However, diagnosing the ECG recordings, which is a demanding and time-consuming job, is mainly relied on experienced medical workers. It is of great importance to develop methods to assist in diagnosing ECG recordings. In this work, we propose a lightweight model architecture based on the Densely Connected Convolutional Networks to classify cardiac arrhythmia automatically. The Cyclical Learning Rate was utilized to automatically change the learning rate value throughout the training procedure. Our model was trained and tested on the PhysioNet/Computing in Cardiology Challenge 2017 (CinC2017) and 1st China Physiological Signal Challenge 2018 (ICBEB2018) datasets. The obtained results have been compared with other works using metric F1 for CinC2017 and metrics F1,AUC,Fmax,Fβ=2,Gβ=2 for ICBEB2018. Though our structure consists of a low number of parameters and requires less computational costs, its performance is in line with the state-of-the-art Deep Neural Networks with F1 score of 0.831 and 0.826 for CinC2017 and ICBEB2018 datasets, respectively.