  This study targets to automatically annotate on arrhythmia by deep network.
The investigated types include sinus rhythm, asystole (Asys), supraventricular
tachycardia (Tachy), ventricular flutter or fibrillation (VF/VFL), ventricular
tachycardia (VT). Methods: 13s limb lead ECG chunks from MIT malignant
ventricular arrhythmia database (VFDB) and MIT normal sinus rhythm database
were partitioned into subsets for 5-fold cross validation. These signals were
resampled to 200Hz, filtered to remove baseline wandering, projected to 2D gray
spectrum and then fed into a deep network with brand-new structure. In this
network, a feature vector for a single time point was retrieved by residual
layers, from which latent representation was extracted by variational
autoencoder (VAE). These front portions were trained to meet a certain
threshold in loss function, then fixed while training procedure switched to
remaining bidirectional recurrent neural network (RNN), the very portions to
predict an arrhythmia category. Attention windows were polynomial lumped on RNN
outputs for learning from details to outlines. And over sampling was employed
for imbalanced data. The trained model was wrapped into docker image for
deployment in edge or cloud. Conclusion: Promising sensitivities were achieved
in four arrhythmias and good precision rates in two ventricular arrhythmias
were also observed. Moreover, it was proven that latent representation by VAE,
can significantly boost the speed of convergence and accuracy.
