Automated ECG multi-class classification system based on combining deep learning features with HRV and ECG measures
AS Eltrass, MB Tayel, AI Ammar - Neural Computing and Applications, 2022 - Springer
Electrocardiogram (ECG) serves as the gold standard for noninvasive diagnosis of several
types of heart disorders. In this study, a novel hybrid approach of deep neural network …
types of heart disorders. In this study, a novel hybrid approach of deep neural network …
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 …
EEGDnet: Fusing non-local and local self-similarity for EEG signal denoising with transformer
Electroencephalogram (EEG) has shown a useful approach to produce a brain–computer
interface (BCI). One-dimensional (1-D) EEG signal is yet easily disturbed by certain artifacts …
interface (BCI). One-dimensional (1-D) EEG signal is yet easily disturbed by certain artifacts …
Dynamic opposite learning enhanced artificial ecosystem optimizer for IIR system identification
Y Niu, X Yan, Y Wang, Y Niu - The Journal of Supercomputing, 2022 - Springer
The contrive principle of the adaptive infinite impulse response (IIR) filter is to find the filter
parameters based on the error function, thus obtaining the best model of the unbeknown …
parameters based on the error function, thus obtaining the best model of the unbeknown …
Novel cascade filter design of improved sparse low-rank matrix estimation and kernel adaptive filtering for ECG denoising and artifacts cancellation
AS Eltrass - Biomedical Signal Processing and Control, 2022 - Elsevier
ElectroCardioGram (ECG) signals are highly vulnerable to disturbances caused by noise
and artifact sources which can degrade the ECG signal quality and increase the difficulty in …
and artifact sources which can degrade the ECG signal quality and increase the difficulty in …
Automatic epileptic seizure detection approach based on multi-stage Quantized Kernel Least Mean Square filters
AS Eltrass, MB Tayel, AF EL-qady - Biomedical Signal Processing and …, 2021 - Elsevier
Epilepsy is one of the most common neurological disorders of the brain all over the world.
For its detection, Electroencephalogram (EEG) is an important noninvasive diagnostic …
For its detection, Electroencephalogram (EEG) is an important noninvasive diagnostic …
Identification and classification of epileptic EEG signals using invertible constant-Q transform-based deep convolutional neural network
AS Eltrass, MB Tayel, AF El-Qady - Journal of Neural Engineering, 2022 - iopscience.iop.org
Context. Epilepsy is the most widespread disorder of the nervous system, affecting humans
of all ages and races. The most common diagnostic test in epilepsy is the …
of all ages and races. The most common diagnostic test in epilepsy is the …
A personalized semi-automatic sleep spindle detection (PSASD) framework
MM Kafashan, G Gupte, P Kang, O Hyche… - Journal of Neuroscience …, 2024 - Elsevier
Background Sleep spindles are distinct electroencephalogram (EEG) patterns of brain
activity that have been posited to play a critical role in development, learning, and …
activity that have been posited to play a critical role in development, learning, and …
Investigation of automatic spindle detection in sleep EEG signals contaminated with noise and artifact sources
AS Eltrass, NH Ghanem - Journal of Ambient Intelligence and Humanized …, 2023 - Springer
Electroencephalography (EEG) serves as the gold standard for noninvasive diagnosis of
different types of sleep disorders such as sleep apnea, insomnia, narcolepsy, restless leg …
different types of sleep disorders such as sleep apnea, insomnia, narcolepsy, restless leg …
A regression model combined convolutional neural network and recurrent neural network for electroencephalogram-based cross-subject fatigue detection
D Yuan, J Yue, H Xu, Y Wang, P Zan… - Review of Scientific …, 2023 - pubs.aip.org
Fatigue, one of the most important factors affecting road safety, has attracted many
researchers' attention. Most existing fatigue detection methods are based on feature …
researchers' attention. Most existing fatigue detection methods are based on feature …