  A key technology enabling the success of catheter ablation treatment for
atrial tachycardia is activation mapping, which relies on manual local
activation time (LAT) annotation of all acquired intracardiac electrogram (EGM)
signals. This is a time-consuming and error-prone procedure, due to the
difficulty in identifying the signal activation peaks for fractionated signals.
This work presents a Deep Learning approach for the automated classification of
EGM signals into three different types: normal, abnormal, and unclassified,
which forms part of the LAT annotation pipeline, and contributes towards
bypassing the need for manual annotations of the LAT. The Deep Learning
network, the CNN-LSTM model, is a hybrid network architecture which combines
convolutional neural network (CNN) layers with long short-term memory (LSTM)
layers. 1452 EGM signals from a total of 9 patients undergoing
clinically-indicated 3D cardiac mapping were used for the training, validation
and testing of our models. From our findings, the CNN-LSTM model achieved an
accuracy of 81% for the balanced dataset. For comparison, we separately
developed a rule-based Decision Trees model which attained an accuracy of 67%
for the same balanced dataset. Our work elucidates that analysing the EGM
signals using a set of explicitly specified rules as proposed by the Decision
Trees model is not suitable as EGM signals are complex. The CNN-LSTM model, on
the other hand, has the ability to learn the complex, intrinsic features within
the signals and identify useful features to differentiate the EGM signals.
