AttentionCovidNet: Efficient ECG-based diagnosis of COVID-19
The novel coronavirus caused a worldwide pandemic. Rapid detection of COVID-19 can
help reduce the spread of the novel coronavirus as well as the burden on healthcare …
help reduce the spread of the novel coronavirus as well as the burden on healthcare …
Towards federated transfer learning in electrocardiogram signal analysis
Modern methods in artificial intelligence perform very well on many healthcare datasets, at
times outperforming trained doctors. However, many assumptions made in model training …
times outperforming trained doctors. However, many assumptions made in model training …
Elimination of Random Mixed Noise in ECG using Convolutional Denoising Autoencoder with Transformer Encoder
M Chen, Y Li, L Zhang, L Liu, B Han… - IEEE Journal of …, 2024 - ieeexplore.ieee.org
Electrocardiogram (ECG) signals frequently encounter diverse types of noise, such as
baseline wander (BW), electrode motion (EM) artifacts, muscle artifact (MA), and others …
baseline wander (BW), electrode motion (EM) artifacts, muscle artifact (MA), and others …
An improved autoencoder for denoising acoustic emission signals in rock fracturing
T Wang, Y Qin, W Zhao… - Nondestructive …, 2024 - Taylor & Francis
Rock fracture acoustic emission (AE) signals are commonly used non-destructive testing
data in geological exploration, resource exploitation, and engineering fields. However, these …
data in geological exploration, resource exploitation, and engineering fields. However, these …
Frequency information enhanced half instance normalization network for denoising electrocardiograms
N Gao, Y Li, N Zheng, W Shi, D Cai, X Huang… - … Signal Processing and …, 2025 - Elsevier
Background Electrocardiogram (ECG) is crucial in diagnosing and preventing heart
diseases. However, its efficacy is compromised by the interference of the external …
diseases. However, its efficacy is compromised by the interference of the external …
Speckle Noise Reduction for Medical Ultrasound Images Using Hybrid CNN-Transformer Network
Ultrasound images are often affected by limited resolution, artifacts, and inherent speckle
noise. To address these challenges, researchers have explored denoising approaches …
noise. To address these challenges, researchers have explored denoising approaches …
Multitask Learning-Based Quality Assessment and Denoising of Electrocardiogram Signals
M Chen, Y Li, L Zhang, X Zhang, J Gao… - IEEE Transactions …, 2024 - ieeexplore.ieee.org
In recent years, there has been a surge in applying deep learning (DL) methods for signal
quality assessment (SQA) and denoising of electrocardiogram (ECG) signals. However …
quality assessment (SQA) and denoising of electrocardiogram (ECG) signals. However …
Ambulatory ECG noise reduction algorithm for conditional diffusion model based on multi-kernel convolutional transformer
H Wang, J Zhang, X Dong, T Wang, X Ma… - Review of Scientific …, 2024 - pubs.aip.org
Ambulatory electrocardiogram (ECG) testing plays a crucial role in the early detection,
diagnosis, treatment evaluation, and prevention of cardiovascular diseases. Clear ECG …
diagnosis, treatment evaluation, and prevention of cardiovascular diseases. Clear ECG …
[图书][B] Vertical federated learning using autoencoders with applications in electrocardiograms
WW Chorney - 2023 - search.proquest.com
Federated learning is a framework in machine learning that allows for training a model while
maintaining data privacy. Moreover, it allows clients with their own data to collaborate in …
maintaining data privacy. Moreover, it allows clients with their own data to collaborate in …
Convolutional Block Attention BiLSTM for Arrhythmia Detection
W Chorney - Studies in Medical and Health Sciences, 2024 - sabapub.com
Cardiovascular diseases represent a significant cause of mortality, with millions of
electrocardiograms being recorded each year. Therefore, methods of automated diagnosis …
electrocardiograms being recorded each year. Therefore, methods of automated diagnosis …