  Electrocardiogram (ECG) signal is a common and powerful tool to study heart
function and diagnose several abnormal arrhythmias. While there have been
remarkable improvements in cardiac arrhythmia classification methods, they
still cannot offer acceptable performance in detecting different heart
conditions, especially when dealing with imbalanced datasets. In this paper, we
propose a solution to address this limitation of current classification
approaches by developing an automatic heartbeat classification method using
deep convolutional neural networks and sequence to sequence models. We
evaluated the proposed method on the MIT-BIH arrhythmia database, considering
the intra-patient and inter-patient paradigms, and the AAMI EC57 standard. The
evaluation results for both paradigms show that our method achieves the best
performance in the literature (a positive predictive value of 96.46% and
sensitivity of 100% for the category S, and a positive predictive value of
98.68% and sensitivity of 97.40% for the category F for the intra-patient
scheme; a positive predictive value of 92.57% and sensitivity of 88.94% for the
category S, and a positive predictive value of 99.50% and sensitivity of 99.94%
for the category V for the inter-patient scheme.). The source code is available
at https://github.com/SajadMo/ECG-Heartbeat-Classification-seq2seq-model.
