Reviewing methods of deep learning for diagnosing COVID-19, its variants and synergistic medicine combinations
Q Rafique, A Rehman, MS Afghan, HM Ahmad… - Computers in Biology …, 2023 - Elsevier
The COVID-19 pandemic has necessitated the development of reliable diagnostic methods
for accurately detecting the novel coronavirus and its variants. Deep learning (DL) …
for accurately detecting the novel coronavirus and its variants. Deep learning (DL) …
Computerized detection of cyclic alternating patterns of sleep: A new paradigm, future scope and challenges
Background and objectives: Sleep quality is associated with wellness, and its assessment
can help diagnose several disorders and diseases. Sleep analysis is commonly performed …
can help diagnose several disorders and diseases. Sleep analysis is commonly performed …
Deep learning in EEG: Advance of the last ten-year critical period
Deep learning has achieved excellent performance in a wide range of domains, especially
in speech recognition and computer vision. Relatively less work has been done for …
in speech recognition and computer vision. Relatively less work has been done for …
Automatic sleep-stage scoring in healthy and sleep disorder patients using optimal wavelet filter bank technique with EEG signals
Sleep stage classification plays a pivotal role in effective diagnosis and treatment of sleep
related disorders. Traditionally, sleep scoring is done manually by trained sleep scorers. The …
related disorders. Traditionally, sleep scoring is done manually by trained sleep scorers. The …
L-SeqSleepNet: Whole-cycle long sequence modelling for automatic sleep staging
Human sleep is cyclical with a period of approximately 90 minutes, implying long temporal
dependency in the sleep data. Yet, exploring this long-term dependency when developing …
dependency in the sleep data. Yet, exploring this long-term dependency when developing …
Detection of K-complexes in EEG signals using deep transfer learning and YOLOv3
The K-complex is one of the most important and noticeable features in the
electroencephalography (EEG) signal, therefore its detection is critical for EEG signal …
electroencephalography (EEG) signal, therefore its detection is critical for EEG signal …
Automated detection of cyclic alternating pattern and classification of sleep stages using deep neural network
The visual sleep stages scoring by human experts is the current gold standard for sleep
analysis. However, this method is tedious, time-consuming, prone to human errors, and …
analysis. However, this method is tedious, time-consuming, prone to human errors, and …
New and emerging approaches to better define sleep disruption and its consequences
Current approaches to quantify and diagnose sleep disorders and circadian rhythm
disruption are imprecise, laborious, and often do not relate well to key clinical and health …
disruption are imprecise, laborious, and often do not relate well to key clinical and health …
CAPSCNet: A novel scattering network for automated identification of phasic cyclic alternating patterns of human sleep using multivariate EEG signals
Abstract The Cyclic Alternating Pattern (CAP) can be considered a physiological marker of
sleep instability. The CAP can examine various sleep-related disorders. Certain short events …
sleep instability. The CAP can examine various sleep-related disorders. Certain short events …
Automated characterization of cyclic alternating pattern using wavelet-based features and ensemble learning techniques with eeg signals
Sleep is highly essential for maintaining metabolism of the body and mental balance for
increased productivity and concentration. Often, sleep is analyzed using macrostructure …
increased productivity and concentration. Often, sleep is analyzed using macrostructure …