  For the weakly supervised task of electrocardiogram (ECG) rhythm
classification, convolutional neural networks (CNNs) and long short-term memory
(LSTM) networks are two increasingly popular classification models. This work
investigates whether a combination of both architectures to so-called
convolutional long short-term memory (ConvLSTM) networks can improve
classification performances by explicitly capturing morphological as well as
temporal features of raw ECG records. In addition, various attention mechanisms
are studied to localize and visualize record sections of abnormal morphology
and irregular rhythm. The resulting saliency maps are supposed to not only
allow for a better network understanding but to also improve clinicians'
acceptance of automatic diagnosis in order to avoid the technique being labeled
as a black box. In further experiments, attention mechanisms are actively
incorporated into the training process by learning a few additional attention
gating parameters in a CNN model. An 8-fold cross validation is finally carried
out on the PhysioNet Computing in Cardiology (CinC) challenge 2017 to compare
the performances of standard CNN models, ConvLSTMs, and attention gated CNNs.
