Beyond supervised learning for pervasive healthcare
The integration of machine/deep learning and sensing technologies is transforming
healthcare and medical practice. However, inherent limitations in healthcare data, namely …
healthcare and medical practice. However, inherent limitations in healthcare data, namely …
Image de-noising with machine learning: A review
Images are susceptible to various kinds of noises, which corrupt the pictorial information
stored in the images. Image de-noising has become an integral part of the image processing …
stored in the images. Image de-noising has become an integral part of the image processing …
Arrhythmia classification algorithm based on multi-head self-attention mechanism
Cardiovascular disease is a major illness that causes human death, especially in the elderly.
Timely and accurate diagnosis of arrhythmia types is the key to early prevention and …
Timely and accurate diagnosis of arrhythmia types is the key to early prevention and …
Attention-based convolutional denoising autoencoder for two-lead ECG denoising and arrhythmia classification
P Singh, A Sharma - IEEE Transactions on Instrumentation and …, 2022 - ieeexplore.ieee.org
This article presents a fast and accurate electrocardiogram (ECG) denoising and
classification method for low-quality ECG signals. To achieve this, a novel attention-based …
classification method for low-quality ECG signals. To achieve this, a novel attention-based …
HARDC: A novel ECG-based heartbeat classification method to detect arrhythmia using hierarchical attention based dual structured RNN with dilated CNN
Deep learning-based models have achieved significant success in detecting cardiac
arrhythmia by analyzing ECG signals to categorize patient heartbeats. To improve the …
arrhythmia by analyzing ECG signals to categorize patient heartbeats. To improve the …
Impulsive mode decomposition
Pulse components commonly exist in natural signals and their extraction has received
extensive concerns in many domains, such as machinery fault diagnosis, ECG denoising …
extensive concerns in many domains, such as machinery fault diagnosis, ECG denoising …
A deep learning-based framework For ECG signal denoising based on stacked cardiac cycle tensor
A Rasti-Meymandi, A Ghaffari - Biomedical Signal Processing and Control, 2022 - Elsevier
The Electrocardiogram (ECG) signal is one of the frequently used non-invasive
physiological measurement techniques for heart diagnosis. However, ECG signal is often …
physiological measurement techniques for heart diagnosis. However, ECG signal is often …
Estimating age and gender from electrocardiogram signals: A comprehensive review of the past decade
Twelve lead electrocardiogram signals capture unique fingerprints about the body's
biological processes and electrical activity of heart muscles. Machine learning and deep …
biological processes and electrical activity of heart muscles. Machine learning and deep …
Occupant-centered indoor environmental quality management: Physiological response measuring methods
Recent studies have reported that occupants' physiological responses can be indicators of
indoor environmental quality (IEQ). As a result, there is an emerging demand for devices to …
indoor environmental quality (IEQ). As a result, there is an emerging demand for devices to …
Accurate wavelet thresholding method for ECG signals
K Yu, L Feng, Y Chen, M Wu, Y Zhang, P Zhu… - Computers in Biology …, 2024 - Elsevier
Current wavelet thresholding methods for cardiogram signals captured by flexible wearable
sensors face a challenge in achieving both accurate thresholding and real-time signal …
sensors face a challenge in achieving both accurate thresholding and real-time signal …