The Effect of Data Augmentation on Classification of Atrial Fibrillation
in Short Single-Lead ECG Signals Using Deep Neural Networks
- URL: http://arxiv.org/abs/2002.02870v2
- Date: Thu, 13 Feb 2020 13:45:16 GMT
- Title: The Effect of Data Augmentation on Classification of Atrial Fibrillation
in Short Single-Lead ECG Signals Using Deep Neural Networks
- Authors: Faezeh Nejati Hatamian, Nishant Ravikumar, Sulaiman Vesal, Felix P.
Kemeth, Matthias Struck, Andreas Maier
- Abstract summary: We investigate the impact of various data augmentation algorithms, e.g., oversampling, on solving the class imbalance problem.
The results show that deep learning-based AF signal classification methods benefit more from data augmentation using GANs and GMMs, than oversampling.
- Score: 12.39263432933148
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Cardiovascular diseases are the most common cause of mortality worldwide.
Detection of atrial fibrillation (AF) in the asymptomatic stage can help
prevent strokes. It also improves clinical decision making through the delivery
of suitable treatment such as, anticoagulant therapy, in a timely manner. The
clinical significance of such early detection of AF in electrocardiogram (ECG)
signals has inspired numerous studies in recent years, of which many aim to
solve this task by leveraging machine learning algorithms. ECG datasets
containing AF samples, however, usually suffer from severe class imbalance,
which if unaccounted for, affects the performance of classification algorithms.
Data augmentation is a popular solution to tackle this problem.
In this study, we investigate the impact of various data augmentation
algorithms, e.g., oversampling, Gaussian Mixture Models (GMMs) and Generative
Adversarial Networks (GANs), on solving the class imbalance problem. These
algorithms are quantitatively and qualitatively evaluated, compared and
discussed in detail. The results show that deep learning-based AF signal
classification methods benefit more from data augmentation using GANs and GMMs,
than oversampling. Furthermore, the GAN results in circa $3\%$ better AF
classification accuracy in average while performing comparably to the GMM in
terms of f1-score.
Related papers
- SQUWA: Signal Quality Aware DNN Architecture for Enhanced Accuracy in Atrial Fibrillation Detection from Noisy PPG Signals [37.788535094404644]
Atrial fibrillation (AF) significantly increases the risk of stroke, heart disease, and mortality.
Photoplethysmography ( PPG) signals are susceptible to corruption from motion artifacts and other factors often encountered in ambulatory settings.
We propose a novel deep learning model, designed to learn how to retain accurate predictions from partially corrupted PPG.
arXiv Detail & Related papers (2024-04-15T01:07:08Z) - GeoECG: Data Augmentation via Wasserstein Geodesic Perturbation for
Robust Electrocardiogram Prediction [20.8603653664403]
We propose a physiologically-inspired data augmentation method to improve performance and increase the robustness of heart disease detection based on ECG signals.
We obtain augmented samples by perturbing the data distribution towards other classes along the geodesic in Wasserstein space.
Learning from 12-lead ECG signals, our model is able to distinguish five categories of cardiac conditions.
arXiv Detail & Related papers (2022-08-02T03:14:13Z) - Preservation of High Frequency Content for Deep Learning-Based Medical
Image Classification [74.84221280249876]
An efficient analysis of large amounts of chest radiographs can aid physicians and radiologists.
We propose a novel Discrete Wavelet Transform (DWT)-based method for the efficient identification and encoding of visual information.
arXiv Detail & Related papers (2022-05-08T15:29:54Z) - MS Lesion Segmentation: Revisiting Weighting Mechanisms for Federated
Learning [92.91544082745196]
Federated learning (FL) has been widely employed for medical image analysis.
FL's performance is limited for multiple sclerosis (MS) lesion segmentation tasks.
We propose the first FL MS lesion segmentation framework via two effective re-weighting mechanisms.
arXiv Detail & Related papers (2022-05-03T14:06:03Z) - Global ECG Classification by Self-Operational Neural Networks with
Feature Injection [25.15075119957447]
We propose a novel approach for inter-patient ECG classification using a compact 1D Self-Organized Operational Neural Networks (Self-ONNs)
We used 1D Self-ONN layers to automatically learn morphological representations from ECG data, enabling us to capture the shape of the ECG waveform around the R peaks.
Using the MIT-BIH arrhythmia benchmark database, the proposed method achieves the highest classification performance ever achieved.
arXiv Detail & Related papers (2022-04-07T22:49:18Z) - Lung Cancer Lesion Detection in Histopathology Images Using Graph-Based
Sparse PCA Network [93.22587316229954]
We propose a graph-based sparse principal component analysis (GS-PCA) network, for automated detection of cancerous lesions on histological lung slides stained by hematoxylin and eosin (H&E)
We evaluate the performance of the proposed algorithm on H&E slides obtained from an SVM K-rasG12D lung cancer mouse model using precision/recall rates, F-score, Tanimoto coefficient, and area under the curve (AUC) of the receiver operator characteristic (ROC)
arXiv Detail & Related papers (2021-10-27T19:28:36Z) - Robustness of convolutional neural networks to physiological ECG noise [0.0]
The electrocardiogram (ECG) is one of the most widespread diagnostic tools in healthcare and supports the diagnosis of cardiovascular disorders.
Deep learning methods are a successful and popular technique to detect indications of disorders from an ECG signal.
There are open questions around the robustness of these methods to various factors, including physiological ECG noise.
We generate clean and noisy versions of an ECG dataset before applying Symmetric Projection Attractor Reconstruction (SPAR) and scalogram image transformations.
A pretrained convolutional neural network is trained using transfer learning to classify these image transforms.
arXiv Detail & Related papers (2021-08-02T08:16:32Z) - Snippet Policy Network for Multi-class Varied-length ECG Early
Classification [8.36820636096359]
Arrhythmia detection from ECG is an important research subject in the prevention and diagnosis of cardiovascular diseases.
We propose a deep reinforcement learning-based framework, namely Snippet Policy Network (SPN), consisting of four modules, snippet generator, backbone network, controlling agent, and discriminator.
Experimental results demonstrate that SPN achieves an excellent performance of over 80% in terms of accuracy.
arXiv Detail & Related papers (2021-07-28T13:47:31Z) - Bootstrapping Your Own Positive Sample: Contrastive Learning With
Electronic Health Record Data [62.29031007761901]
This paper proposes a novel contrastive regularized clinical classification model.
We introduce two unique positive sampling strategies specifically tailored for EHR data.
Our framework yields highly competitive experimental results in predicting the mortality risk on real-world COVID-19 EHR data.
arXiv Detail & Related papers (2021-04-07T06:02:04Z) - Predictive Modeling of ICU Healthcare-Associated Infections from
Imbalanced Data. Using Ensembles and a Clustering-Based Undersampling
Approach [55.41644538483948]
This work is focused on both the identification of risk factors and the prediction of healthcare-associated infections in intensive-care units.
The aim is to support decision making addressed at reducing the incidence rate of infections.
arXiv Detail & Related papers (2020-05-07T16:13:12Z) - Opportunities and Challenges of Deep Learning Methods for
Electrocardiogram Data: A Systematic Review [62.490310870300746]
The electrocardiogram (ECG) is one of the most commonly used diagnostic tools in medicine and healthcare.
Deep learning methods have achieved promising results on predictive healthcare tasks using ECG signals.
This paper presents a systematic review of deep learning methods for ECG data from both modeling and application perspectives.
arXiv Detail & Related papers (2019-12-28T02:44:29Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.