[HTML][HTML] IC-U-Net: a U-Net-based denoising autoencoder using mixtures of independent components for automatic EEG artifact removal
Electroencephalography (EEG) signals are often contaminated with artifacts. It is imperative
to develop a practical and reliable artifact removal method to prevent the misinterpretation of …
to develop a practical and reliable artifact removal method to prevent the misinterpretation of …
An ECG denoising method based on adversarial denoising convolutional neural network
Y Hou, R Liu, M Shu, C Chen - Biomedical Signal Processing and Control, 2023 - Elsevier
The Electrocardiogram (ECG) is widely used to diagnose heart disease. However, ECG
recordings are often polluted by different noises in real situations. It is of great significance to …
recordings are often polluted by different noises in real situations. It is of great significance to …
[HTML][HTML] Inferring ECG Waveforms from PPG Signals with a Modified U-Net Neural Network
There are two widely used methods to measure the cardiac cycle and obtain heart rate
measurements: the electrocardiogram (ECG) and the photoplethysmogram (PPG). The …
measurements: the electrocardiogram (ECG) and the photoplethysmogram (PPG). The …
Band-stop smoothing filter design
AK Roonizi, C Jutten - IEEE Transactions on Signal Processing, 2021 - ieeexplore.ieee.org
Smoothness priors and quadratic variation (QV) regularization are widely used techniques
in many applications ranging from signal and image processing, computer vision, pattern …
in many applications ranging from signal and image processing, computer vision, pattern …
Evaluation of capacitive ECG for unobtrusive atrial fibrillation monitoring
Unobtrusive collection of vital signs using sensors embedded in beds, chairs, and
automobile seats can longitudinally monitor patients for abnormal heart conditions outside of …
automobile seats can longitudinally monitor patients for abnormal heart conditions outside of …
Design of deep convolutional neural network architectures for denoising electrocardiographic signals
C Arsene - … IEEE Conference on Computational Intelligence in …, 2020 - ieeexplore.ieee.org
Deep Learning (DL) models have been used extensively in image processing and other
domains with great success but only very recently have been used in processing …
domains with great success but only very recently have been used in processing …
Multiple channel electrocardiogram QRS detection by temporal pattern search
B Hopenfeld - bioRxiv, 2021 - biorxiv.org
A highly constrained temporal pattern search (“TEPS”) based multiple channel heartbeat
detector is described. TEPS generates sequences of peaks and statistically scores them …
detector is described. TEPS generates sequences of peaks and statistically scores them …
Classification of ECG Signal for Cardiac Arrhythmia Detection Using GAN Method
ST Sanamdikar, NM Karajanagi, KH Kowdiki… - … and Virtual Mobile …, 2022 - Springer
Today, a big number of people suffer from various cardiac problems all over the world. As a
result, knowing how the ECG signal works is critical for recognising a number of heart …
result, knowing how the ECG signal works is critical for recognising a number of heart …
一种利用改进深度图像先验构建的图像降噪模型
徐少平， 李芬， 陈孝国， 陈晓军， 江顺亮 - 电子学报, 2022 - ejournal.org.cn
为进一步提高深度图像先验(Deep Image Prior, DIP) 降噪模型的降噪效果和执行效率,
从网络结构, 网络输入和Loss 函数三个方面对其进行改进从而获得了一种改进的深度图像先验 …
从网络结构, 网络输入和Loss 函数三个方面对其进行改进从而获得了一种改进的深度图像先验 …
Are all shortcuts in encoder–decoder networks beneficial for CT denoising?
Denoising of CT scans has attracted the attention of many researchers in the medical image
analysis domain. Encoder–decoder networks are deep learning neural networks that have …
analysis domain. Encoder–decoder networks are deep learning neural networks that have …