  Cardiac resynchronization therapy (CRT) is a treatment that is used to
compensate for irregularities in the heartbeat. Studies have shown that this
treatment is more effective in heart patients with left bundle branch block
(LBBB) arrhythmia. Therefore, identifying this arrhythmia is an important
initial step in determining whether or not to use CRT. On the other hand,
traditional methods for detecting LBBB on electrocardiograms (ECG) are often
associated with errors. Thus, there is a need for an accurate method to
diagnose this arrhythmia from ECG data. Machine learning, as a new field of
study, has helped to increase human systems' performance. Deep learning, as a
newer subfield of machine learning, has more power to analyze data and increase
systems accuracy. This study presents a deep learning model for the detection
of LBBB arrhythmia from 12-lead ECG data. This model consists of 1D dilated
convolutional layers. Attention mechanism has also been used to identify
important input data features and classify inputs more accurately. The proposed
model is trained and validated on a database containing 10344 12-lead ECG
samples using the 10-fold cross-validation method. The final results obtained
by the model on the 12-lead ECG data are as follows. Accuracy: 98.80+-0.08%,
specificity: 99.33+-0.11 %, F1 score: 73.97+-1.8%, and area under the receiver
operating characteristics curve (AUC): 0.875+-0.0192. These results indicate
that the proposed model in this study can effectively diagnose LBBB with good
efficiency and, if used in medical centers, will greatly help diagnose this
arrhythmia and early treatment.
