A comprehensive survey on ECG signals as new biometric modality for human authentication: Recent advances and future challenges
AN Uwaechia, DA Ramli - IEEE Access, 2021 - ieeexplore.ieee.org
Electrocardiogram (ECG) has extremely discriminative characteristics in the biometric field
and has recently received significant interest as a promising biometric trait. However, ECG …
and has recently received significant interest as a promising biometric trait. However, ECG …
[LIVRE][B] The illustrated wavelet transform handbook: introductory theory and applications in science, engineering, medicine and finance
PS Addison - 2017 - taylorfrancis.com
This second edition of The Illustrated Wavelet Transform Handbook: Introductory Theory and
Applications in Science, Engineering, Medicine and Finance has been fully updated and …
Applications in Science, Engineering, Medicine and Finance has been fully updated and …
Deep learning-based stacked denoising and autoencoder for ECG heartbeat classification
S Nurmaini, A Darmawahyuni, AN Sakti Mukti… - Electronics, 2020 - mdpi.com
The electrocardiogram (ECG) is a widely used, noninvasive test for analyzing arrhythmia.
However, the ECG signal is prone to contamination by different kinds of noise. Such noise …
However, the ECG signal is prone to contamination by different kinds of noise. Such noise …
[HTML][HTML] Robust detection of atrial fibrillation from short-term electrocardiogram using convolutional neural networks
The most prevalent arrhythmia observed in clinical practice is atrial fibrillation (AF). AF is
associated with an irregular heartbeat pattern and a lack of a distinct P-waves signal. A low …
associated with an irregular heartbeat pattern and a lack of a distinct P-waves signal. A low …
Recent advancements in multimodal human–robot interaction
Robotics have advanced significantly over the years, and human–robot interaction (HRI) is
now playing an important role in delivering the best user experience, cutting down on …
now playing an important role in delivering the best user experience, cutting down on …
A novel hybrid deep learning method with cuckoo search algorithm for classification of arrhythmia disease using ECG signals
This work presents an efficient hybridized approach for the classification of
electrocardiogram (ECG) samples into crucial arrhythmia classes to detect heartbeat …
electrocardiogram (ECG) samples into crucial arrhythmia classes to detect heartbeat …
Time–frequency localized three-band biorthogonal wavelet filter bank using semidefinite relaxation and nonlinear least squares with epileptic seizure EEG signal …
In this paper, we design time–frequency localized three-band biorthogonal linear phase
wavelet filter bank for epileptic seizure electroencephalograph (EEG) signal classification …
wavelet filter bank for epileptic seizure electroencephalograph (EEG) signal classification …
The optimal selection of mother wavelet function and decomposition level for denoising of DCG signal
The aim of this paper is to find the optimal mother wavelet function and wavelet
decomposition level when denoising the Doppler cardiogram (DCG), the heart signal …
decomposition level when denoising the Doppler cardiogram (DCG), the heart signal …
Generalized total variation-based MRI Rician denoising model with spatially adaptive regularization parameters
Magnetic resonance imaging (MRI) is an outstanding medical imaging modality but the
quality often suffers from noise pollution during image acquisition and transmission. The …
quality often suffers from noise pollution during image acquisition and transmission. The …
ECG signal performance de-noising assessment based on threshold tuning of dual-tree wavelet transform
O El B'charri, R Latif, K Elmansouri, A Abenaou… - Biomedical engineering …, 2017 - Springer
Background Since the electrocardiogram (ECG) signal has a low frequency and a weak
amplitude, it is sensitive to miscellaneous mixed noises, which may reduce the diagnostic …
amplitude, it is sensitive to miscellaneous mixed noises, which may reduce the diagnostic …