Review of noise removal techniques in ECG signals
An electrocardiogram (ECG) records the electrical signal from the heart to check for different
heart conditions, but it is susceptible to noises. ECG signal denoising is a major pre …
heart conditions, but it is susceptible to noises. ECG signal denoising is a major pre …
Computational diagnostic techniques for electrocardiogram signal analysis
Cardiovascular diseases (CVDs), including asymptomatic myocardial ischemia, angina,
myocardial infarction, and ischemic heart failure, are the leading cause of death globally …
myocardial infarction, and ischemic heart failure, are the leading cause of death globally …
Automatic cardiac arrhythmia classification based on hybrid 1-D CNN and Bi-LSTM model
Cardiovascular diseases (CVDs) are a group of heart and blood vessel ailments that can
cause chest pain and trouble breathing, especially while active. However, some patients …
cause chest pain and trouble breathing, especially while active. However, some patients …
A review on computational methods for denoising and detecting ECG signals to detect cardiovascular diseases
Cardiac health of the human heart is an intriguing issue for many decades as cardiovascular
diseases (CVDs) are the leading cause of death worldwide. Electrocardiogram (ECG) signal …
diseases (CVDs) are the leading cause of death worldwide. Electrocardiogram (ECG) signal …
Capsule network assisted electrocardiogram classification model for smart healthcare
Improving the classification accuracy of electrocardiogram (ECG) signals is of great
significance for diagnosing heart abnormalities and arrhythmias and preventing …
significance for diagnosing heart abnormalities and arrhythmias and preventing …
Automatic muscle artifacts identification and removal from single-channel eeg using wavelet transform with meta-heuristically optimized non-local means filter
Electroencephalogram (EEG) signals may get easily contaminated by muscle artifacts,
which may lead to wrong interpretation in the brain–computer interface (BCI) system as well …
which may lead to wrong interpretation in the brain–computer interface (BCI) system as well …
Hybrid short-term load forecasting method based on empirical wavelet transform and bidirectional long short-term memory neural networks
Accurate short-term load forecasting is essential to modern power systems and smart grids.
The utility can better implement demand-side management and operate power system …
The utility can better implement demand-side management and operate power system …
High voltage shunt reactor acoustic signal denoising based on the combination of VMD parameters optimized by coati optimization algorithm and wavelet threshold
W Lei, G Wang, B Wan, Y Min, J Wu, B Li - Measurement, 2024 - Elsevier
The goal of this research is the application of variational mode decomposition (VMD) in the
denoising process of acoustic signals from high-voltage shunt reactors. This paper propose …
denoising process of acoustic signals from high-voltage shunt reactors. This paper propose …
Detection of Ventricular Arrhythmia by using Heart rate variability signal and ECG beat image
Ventricular Arrhythmia (VA) such as Ventricular Tachycardia (VT) and Ventricular Fibrillation
(VF) are the common type of arrhythmia in infants and children. Electrocardiogram (ECG) …
(VF) are the common type of arrhythmia in infants and children. Electrocardiogram (ECG) …
Sparsity-based modified wavelet de-noising autoencoder for ECG signals
Electrocardiogram (ECG) is susceptible to different kinds of noises whose removal is
necessary for accurate clinical diagnosis. This paper proposes a hybrid technique that …
necessary for accurate clinical diagnosis. This paper proposes a hybrid technique that …