Classification of Cardiac Arrhythmias from Single Lead ECG with a Convolutional Recurrent Neural Network

Jérôme Van Zaen, Olivier Chételat, Mathieu Lemay, Enric M. Calvo, Ricard Delgado-Gonzalo

2019

Abstract

While most heart arrhythmias are not immediately harmful, they can lead to severe complications. In particular, atrial fibrillation, the most common arrhythmia, is characterized by fast and irregular heart beats and increases the risk of suffering a stroke. To detect such abnormal heart conditions, we propose a system composed of two main parts: a smart vest with two cooperative sensors to collect ECG data and a neural network architecture to classify heart rhythms. The smart vest uses two dry bi-electrodes to record a single lead ECG signal. The biopotential signal is then streamed via a gateway to the cloud where a neural network detects and classifies the heart arrhythmias. We selected an architecture that combines convolutional and recurrent layers. The convolutional layers extract relevant features from sliding windows of ECG and the recurrent layer aggregates them for a final softmax layer that performs the classification. Our neural network achieves an accuracy of 87.50% on the dataset of the challenge of Computing in Cardiology 2017.

Paper Citation

in Harvard Style

Van Zaen J., Chételat O., Lemay M., Calvo E. and Delgado-Gonzalo R. (2019). Classification of Cardiac Arrhythmias from Single Lead ECG with a Convolutional Recurrent Neural Network. In Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2019) - Volume 4: BIOSIGNALS; ISBN 978-989-758-353-7, SciTePress, pages 33-41. DOI: 10.5220/0007347900330041

in Bibtex Style

@conference{biosignals19,
author={Jérôme Van Zaen and Olivier Chételat and Mathieu Lemay and Enric M. Calvo and Ricard Delgado-Gonzalo},
title={Classification of Cardiac Arrhythmias from Single Lead ECG with a Convolutional Recurrent Neural Network},
booktitle={Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2019) - Volume 4: BIOSIGNALS},
year={2019},
pages={33-41},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007347900330041},
isbn={978-989-758-353-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2019) - Volume 4: BIOSIGNALS
TI - Classification of Cardiac Arrhythmias from Single Lead ECG with a Convolutional Recurrent Neural Network
SN - 978-989-758-353-7
AU - Van Zaen J.
AU - Chételat O.
AU - Lemay M.
AU - Calvo E.
AU - Delgado-Gonzalo R.
PY - 2019
SP - 33
EP - 41
DO - 10.5220/0007347900330041
PB - SciTePress