# Convolutional Recurrent Neural Networks for Electrocardiogram Classification

arXiv: https://arxiv.org/abs/1710.06122 · HTML: https://mlsanity.com/paper/1710.06122/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Oct 2017.

## Built on by (most cited first, 14 of 14)

- [POPNASv3: a Pareto-Optimal Neural Architecture Search Solution for Image and Time Series Classification](https://mlsanity.com/paper/2212.06735/) (2022)
- [Semi-Supervised Learning for Multi-Label Cardiovascular Diseases Prediction:A Multi-Dataset Study](https://mlsanity.com/paper/2306.10494/) (2023)
- [ECG-SL: Electrocardiogram(ECG) Segment Learning, a deep learning method for ECG signal](https://mlsanity.com/paper/2310.00818/) (2023)
- [An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future works](https://mlsanity.com/paper/2105.14278/) (2021)
- [Deconvolution-and-convolution Networks](https://mlsanity.com/paper/2103.11887/) (2021)
- [A Policy for Early Sequence Classification](https://mlsanity.com/paper/2304.03463/) (2023)
- [Supraventricular Tachycardia Detection and Classification Model of ECG signal Using Machine Learning](https://mlsanity.com/paper/2112.12953/) (2021)
- [Accuracy Improvement for Fully Convolutional Networks via Selective Augmentation with Applications to Electrocardiogram Data](https://mlsanity.com/paper/2104.12284/) (2021)
- [Machine Learning-based Efficient Ventricular Tachycardia Detection Model of ECG Signal](https://mlsanity.com/paper/2112.12956/) (2021)
- [Towards Personalized Healthcare in Cardiac Population: The Development of a Wearable ECG Monitoring System, an ECG Lossy Compression Schema, and a ResNet-Based AF Detector](https://mlsanity.com/paper/2207.05138/) (2022)
- [From Single-Visit to Multi-Visit Image-Based Models: Single-Visit Models are Enough to Predict Obstructive Hydronephrosis](https://mlsanity.com/paper/2212.13535/) (2022)
- [Enhancing ECG Analysis of Implantable Cardiac Monitor Data: An Efficient Pipeline for Multi-Label Classification](https://mlsanity.com/paper/2307.07423/) (2023)
- [Fold Bifurcation Identification through Scientific Machine Learning](https://mlsanity.com/paper/2312.14210/) (2023)
- [NapTune: Efficient Model Tuning for Mood Classification using Previous Night's Sleep Measures along with Wearable Time-series](https://mlsanity.com/paper/2409.04723/) (2024)

## Abstract (arXiv)

We propose two deep neural network architectures for classification of arbitrary-length electrocardiogram (ECG) recordings and evaluate them on the atrial fibrillation (AF) classification data set provided by the PhysioNet/CinC Challenge 2017. The first architecture is a deep convolutional neural network (CNN) with averaging-based feature aggregation across time. The second architecture combines convolutional layers for feature extraction with long-short term memory (LSTM) layers for temporal aggregation of features. As a key ingredient of our training procedure we introduce a simple data augmentation scheme for ECG data and demonstrate its effectiveness in the AF classification task at hand. The second architecture was found to outperform the first one, obtaining an F1 score of 82.1% on the hidden challenge testing set.