Cardiac Arrhythmia Detection from ECG with Convolutional Recurrent Neural Networks

TL;DR

This paper introduces three neural network architectures combining convolutional and recurrent layers to detect cardiac arrhythmias from ECG signals, achieving high accuracy on public datasets.

Contribution

The paper presents novel neural network architectures that effectively combine convolutional and recurrent layers for arrhythmia detection from ECG signals.

Findings

Achieved 86.23% accuracy on the PhysioNet Challenge dataset.

Achieved 92.02% accuracy on a combined PhysioNet dataset.

Performance comparable to top challenge entries.

Abstract

Except for a few specific types, cardiac arrhythmias are not immediately life-threatening. However, if not treated appropriately, they can cause serious complications. In particular, atrial fibrillation, which is characterized by fast and irregular heart beats, increases the risk of stroke. We propose three neural network architectures to detect abnormal rhythms from single-lead ECG signals. These architectures combine convolutional layers to extract high-level features pertinent for arrhythmia detection from sliding windows and recurrent layers to aggregate these features over signals of varying durations. We applied the neural networks to the dataset used for the challenge of Computing in Cardiology 2017 and a dataset built by joining three databases available on PhysioNet. Our architectures achieved an accuracy of 86.23% on the first dataset, similar to the winning entries of the challenge, and an accuracy of 92.02% on the second dataset.