SLC-GAN: An automated myocardial infarction detection model based on generative adversarial networks and convolutional neural networks with single-lead …
Electrocardiography (ECG) is a sophisticated tool for the diagnosis of myocardial infarction
(MI). Deep learning approaches can support MI diagnosis based on ECG data. However …
(MI). Deep learning approaches can support MI diagnosis based on ECG data. However …
Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network
F Zhu, F Ye, Y Fu, Q Liu, B Shen - Scientific reports, 2019 - nature.com
Heart disease is a malignant threat to human health. Electrocardiogram (ECG) tests are
used to help diagnose heart disease by recording the heart's activity. However, automated …
used to help diagnose heart disease by recording the heart's activity. However, automated …
Hierarchical deep learning with Generative Adversarial Network for automatic cardiac diagnosis from ECG signals
Cardiac disease is the leading cause of death in the US. Accurate heart disease detection is
critical to timely medical treatment to save patients' lives. Routine use of the …
critical to timely medical treatment to save patients' lives. Routine use of the …
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 …
Generalization of convolutional neural networks for ECG classification using generative adversarial networks
Electrocardiograms (ECGs) play a vital role in the clinical diagnosis of heart diseases. An
ECG record of the heart signal over time can be used to discover numerous arrhythmias. Our …
ECG record of the heart signal over time can be used to discover numerous arrhythmias. Our …
MFB-LANN: A lightweight and updatable myocardial infarction diagnosis system based on convolutional neural networks and active learning
Background and objectives: 12 leads electrocardiogram (ECG) are widely used to diagnose
myocardial infarction (MI). Generally, the symptoms of MI can be reflected by waveforms in …
myocardial infarction (MI). Generally, the symptoms of MI can be reflected by waveforms in …
Improving ECG classification using generative adversarial networks
The Electrocardiogram (ECG) is performed routinely by medical personell 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 …
[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 …
Classification of myocardial infarction with multi-lead ECG signals and deep CNN
Myocardial infarction (MI), commonly known as heart attack, causes irreversible damage to
heart muscles and even leads to death. Rapid and accurate diagnosis of MI is critical to …
heart muscles and even leads to death. Rapid and accurate diagnosis of MI is critical to …
A novel temporal generative adversarial network for electrocardiography anomaly detection
Cardiac abnormality detection from Electrocardiogram (ECG) signals is a common task for
cardiologists. To facilitate efficient and objective detection, automated ECG classification by …
cardiologists. To facilitate efficient and objective detection, automated ECG classification by …