Ensembling convolutional and long short-term memory networks for electrocardiogram arrhythmia detection

Atrial fibrillation is a common type of heart rhythm abnormality caused by a problem with the heart's electrical system. Early detection of this disease has important implications for stroke prevention and management. Our objective is to construct an intelligent tool that assists cardiologists in identifying automatically cardiac arrhythmias and noise in electrocardiogram (ECG) recordings.

Our base deep classifier combined a convolutional neural network (CNNs) and a sequence of long short-term memory units, with pooling, dropout and normalization techniques to improve their accuracy. The network predicted a classification at every 18th input sample and the final prediction was selected for classification. Ten standalone models that used our base classifier architecture were first cross-validated separately on 90% of the PhysioNet/CinC Challenge 2017 dataset and then tested on 10%. An ensemble classifier selected the label of the best average probability from the ten sub-models to improve prediction quality.

Our original result submitted to the challenge gave a mean F₁-measure of 80%. The new proposed method improved the test score to 82%, which was tied for the third-highest score in the follow-up phase of the challenge.

Without employing a time-consuming feature engineering step, the ensemble classifier trained with this architecture provided a robust solution to the problem of detecting cardiac arrhythmia from noisy ECG signals. In addition, interpretation of the classifier by inspection of its network parameters and predictions revealed what aspects of the ECG signal the classifier considered most discriminating.