  Objectives: Atrial fibrillation (AF) is a common heart rhythm disorder
associated with deadly and debilitating consequences including heart failure,
stroke, poor mental health, reduced quality of life and death. Having an
automatic system that diagnoses various types of cardiac arrhythmias would
assist cardiologists to initiate appropriate preventive measures and to improve
the analysis of cardiac disease. To this end, this paper introduces a new
approach to detect and classify automatically cardiac arrhythmias in
electrocardiograms (ECG) recordings.
  Methods: The proposed approach used a combination of Convolution Neural
Networks (CNNs) and a sequence of Long Short-Term Memory (LSTM) units, with
pooling, dropout and normalization techniques to improve their accuracy. The
network predicted a classification at every 18th input sample and we selected
the final prediction for classification. Results were cross-validated on the
Physionet Challenge 2017 training dataset, which contains 8,528 single lead ECG
recordings lasting from 9s to just over 60s.
  Results: Using the proposed structure and no explicit feature selection,
10-fold stratified cross-validation gave an overall F-measure of 0.83.10-0.015
on the held-out test data (mean-standard deviation over all folds) and 0.80 on
the hidden dataset of the Challenge entry server.
