A Generative Adversarial Approach To ECG Synthesis And Denoising

• URL: http://arxiv.org/abs/2009.02700v1
• Date: Sun, 6 Sep 2020 10:17:33 GMT
• Title: A Generative Adversarial Approach To ECG Synthesis And Denoising
• Authors: Karol Antczak
• Abstract summary: We present an approach to use GAN to produce realistically looking ECG signals. We utilize them to train and evaluate a denoising autoencoder that achieves state-of-the-art filtering quality for ECG signals.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generative Adversarial Networks (GAN) are known to produce synthetic data that are difficult to discern from real ones by humans. In this paper we present an approach to use GAN to produce realistically looking ECG signals. We utilize them to train and evaluate a denoising autoencoder that achieves state-of-the-art filtering quality for ECG signals. It is demonstrated that generated data improves the model performance compared to the model trained on real data only. We also investigate an effect of transfer learning by reusing trained discriminator network for denoising model.

Related papers

• Flexible framework for generating synthetic electrocardiograms and photoplethysmograms [1.023858929087312]
  We have developed a synthetic biosignal model for two signal modalities, electrocardiography (ECG) and photoplethys (mography) The model produces realistic signals that account for physiological effects such as breathing modulation and changes in heart rate due to physical stress. We trained an LSTM to detect ECG R-peaks using both real ECG signals from the MIT-BIH arrythmia set and our new generator.
  arXiv  Detail & Related papers  (2024-08-29T06:48:07Z)
• SDEMG: Score-based Diffusion Model for Surface Electromyographic Signal Denoising [15.472398279233515]
  Surface electromyography (sEMG) recordings can be influenced by electrocardiogram (ECG) signals when the muscle being monitored is close to the heart. We propose a novel approach, termed SDEMG, as a score-based diffusion model for sEMG signal denoising.
  arXiv  Detail & Related papers  (2024-02-06T08:48:39Z)
• DGSD: Dynamical Graph Self-Distillation for EEG-Based Auditory Spatial Attention Detection [49.196182908826565]
  Auditory Attention Detection (AAD) aims to detect target speaker from brain signals in a multi-speaker environment. Current approaches primarily rely on traditional convolutional neural network designed for processing Euclidean data like images. This paper proposes a dynamical graph self-distillation (DGSD) approach for AAD, which does not require speech stimuli as input.
  arXiv  Detail & Related papers  (2023-09-07T13:43:46Z)
• Convolutional Neural Networks for the classification of glitches in gravitational-wave data streams [52.77024349608834]
  We classify transient noise signals (i.e.glitches) and gravitational waves in data from the Advanced LIGO detectors. We use models with a supervised learning approach, both trained from scratch using the Gravity Spy dataset. We also explore a self-supervised approach, pre-training models with automatically generated pseudo-labels.
  arXiv  Detail & Related papers  (2023-03-24T11:12:37Z)
• Denoising Diffusion Autoencoders are Unified Self-supervised Learners [58.194184241363175]
  This paper shows that the networks in diffusion models, namely denoising diffusion autoencoders (DDAE), are unified self-supervised learners. DDAE has already learned strongly linear-separable representations within its intermediate layers without auxiliary encoders. Our diffusion-based approach achieves 95.9% and 50.0% linear evaluation accuracies on CIFAR-10 and Tiny-ImageNet.
  arXiv  Detail & Related papers  (2023-03-17T04:20:47Z)
• Decision Forest Based EMG Signal Classification with Low Volume Dataset Augmented with Random Variance Gaussian Noise [51.76329821186873]
  We produce a model that can classify six different hand gestures with a limited number of samples that generalizes well to a wider audience. We appeal to a set of more elementary methods such as the use of random bounds on a signal, but desire to show the power these methods can carry in an online setting.
  arXiv  Detail & Related papers  (2022-06-29T23:22:18Z)
• Deceive D: Adaptive Pseudo Augmentation for GAN Training with Limited Data [125.7135706352493]
  Generative adversarial networks (GANs) typically require ample data for training in order to synthesize high-fidelity images. Recent studies have shown that training GANs with limited data remains formidable due to discriminator overfitting. This paper introduces a novel strategy called Adaptive Pseudo Augmentation (APA) to encourage healthy competition between the generator and the discriminator.
  arXiv  Detail & Related papers  (2021-11-12T18:13:45Z)
• Improving the efficacy of Deep Learning models for Heart Beat detection on heterogeneous datasets [0.0]
  We investigate the issues related to applying a Deep Learning model on heterogeneous datasets. We show that the performance of a model trained on data from healthy subjects decreases when applied to patients with cardiac conditions. We then evaluate the use of Transfer Learning to adapt the model to the different datasets.
  arXiv  Detail & Related papers  (2021-10-26T14:26:55Z)
• GANSER: A Self-supervised Data Augmentation Framework for EEG-based Emotion Recognition [15.812231441367022]
  We propose a novel data augmentation framework, namely Generative Adversarial Network-based Self-supervised Data Augmentation (GANSER) As the first to combine adversarial training with self-supervised learning for EEG-based emotion recognition, the proposed framework can generate high-quality simulated EEG samples. A transformation function is employed to mask parts of EEG signals and force the generator to synthesize potential EEG signals based on the remaining parts.
  arXiv  Detail & Related papers  (2021-09-07T14:42:55Z)
• ECG-Adv-GAN: Detecting ECG Adversarial Examples with Conditional Generative Adversarial Networks [4.250203361580781]
  Deep neural networks have become a popular technique for tracing ECG signals, outperforming human experts. GAN architecture has been employed in recent works to synthesize adversarial ECG signals to increase existing training data. We propose a novel Conditional Generative Adrial Network to simultaneously generate ECG signals for different categories and detect cardiac abnormalities.
  arXiv  Detail & Related papers  (2021-07-16T02:53:14Z)
• ECG-DelNet: Delineation of Ambulatory Electrocardiograms with Mixed Quality Labeling Using Neural Networks [69.25956542388653]
  Deep learning (DL) algorithms are gaining weight in academic and industrial settings. We demonstrate DL can be successfully applied to low interpretative tasks by embedding ECG detection and delineation onto a segmentation framework. The model was trained using PhysioNet's QT database, comprised of 105 ambulatory ECG recordings.
  arXiv  Detail & Related papers  (2020-05-11T16:29:12Z)

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.