Leveraging statistical shape priors in gan-based ECG synthesis
Electrocardiogram (ECG) data collection during emergency situations is challenging,
making ECG data generation an efficient solution for dealing with highly imbalanced ECG …
making ECG data generation an efficient solution for dealing with highly imbalanced ECG …
Disentangling temporal and amplitude variations in ECG synthesis using anchored GANs
Electrocardiogram (ECG) synthesis is a challenging task due to the complex dynamic nature
of ECG signals. In this paper, we present a novel approach for ECG synthesis based on …
of ECG signals. In this paper, we present a novel approach for ECG synthesis based on …
Proegan-ms: A progressive growing generative adversarial networks for electrocardiogram generation
Electrocardiogram (ECG) is a physiological signal widely used in monitoring heart health,
which is of great significance to the detection and diagnosis of heart diseases. Because …
which is of great significance to the detection and diagnosis of heart diseases. Because …
Pgans: Personalized generative adversarial networks for ecg synthesis to improve patient-specific deep ecg classification
T Golany, K Radinsky - Proceedings of the AAAI Conference on Artificial …, 2019 - aaai.org
The Electrocardiogram (ECG) is performed routinely by medical personnel to identify
structural, functional and electrical cardiac events. Many attempts were made to automate …
structural, functional and electrical cardiac events. Many attempts were made to automate …
ECG generation with sequence generative adversarial nets optimized by policy gradient
F Ye, F Zhu, Y Fu, B Shen - IEEE Access, 2019 - ieeexplore.ieee.org
Electrocardiogram (ECG) is a method used by physicians to detect cardiac disease.
Requirements for batch processing and accurate recognition of clinical data have led to the …
Requirements for batch processing and accurate recognition of clinical data have led to the …
[HTML][HTML] Generative adversarial networks in electrocardiogram synthesis: Recent developments and challenges
Training deep neural network classifiers for electrocardiograms (ECGs) requires sufficient
data. However, imbalanced datasets pose a major problem for the training process and …
data. However, imbalanced datasets pose a major problem for the training process and …
An Innovative Approach for Long ECG Synthesis with Wasserstein GAN Model
TD Tran, TTK Dang, NQ Tran - International Conference on Computational …, 2024 - Springer
Deep neural networks (DNNs) have set new standards in identifying and classifying
irregular patterns in ECG (electrocardiogram) signals, surpassing previous methods …
irregular patterns in ECG (electrocardiogram) signals, surpassing previous methods …
Synthetic ecg signal generation using generative neural networks
Electrocardiogram (ECG) datasets tend to be highly imbalanced due to the scarcity of
abnormal cases. Additionally, the use of real patients' ECGs is highly regulated due to …
abnormal cases. Additionally, the use of real patients' ECGs is highly regulated due to …
Feature matching based ECG generative network for arrhythmia event augmentation
F Cao, A Budhota, H Chen… - 2020 42nd Annual …, 2020 - ieeexplore.ieee.org
Recent developments in the field of deep learning has shown a rise in its use for clinical
applications such as electrocardiogram (ECG) analysis and cardiac arrhythmia …
applications such as electrocardiogram (ECG) analysis and cardiac arrhythmia …
Synthesis of standard 12‑lead electrocardiograms using two-dimensional generative adversarial networks
YH Zhang, S Babaeizadeh - Journal of Electrocardiology, 2021 - Elsevier
This paper proposes a two-dimensional (2D) bidirectional long short-term memory
generative adversarial network (GAN) to produce synthetic standard 12-lead ECGs …
generative adversarial network (GAN) to produce synthetic standard 12-lead ECGs …