Deep learning in physiological signal data: A survey
Deep Learning (DL), a successful promising approach for discriminative and generative
tasks, has recently proved its high potential in 2D medical imaging analysis; however …
tasks, has recently proved its high potential in 2D medical imaging analysis; however …
Brain injury after cardiac arrest: from prognostication of comatose patients to rehabilitation
T Cronberg, DM Greer, G Lilja, V Moulaert… - The Lancet …, 2020 - thelancet.com
More patients are surviving cardiac arrest than ever before; however, the burden now lies
with estimating neurological prognoses in a large number of patients who were initially …
with estimating neurological prognoses in a large number of patients who were initially …
[HTML][HTML] Multimodal detection of epilepsy with deep neural networks
Epilepsy constitutes a chronic noncommunicable disease of the brain affecting
approximately 50 million people around the world. Most of the existing research initiatives …
approximately 50 million people around the world. Most of the existing research initiatives …
Grad-CAM helps interpret the deep learning models trained to classify multiple sclerosis types using clinical brain magnetic resonance imaging
Background Deep learning using convolutional neural networks (CNNs) has shown great
promise in advancing neuroscience research. However, the ability to interpret the CNNs …
promise in advancing neuroscience research. However, the ability to interpret the CNNs …
Deep learning in EEG: Advance of the last ten-year critical period
S Gong, K ** study
Electroencephalography (EEG) is used in the diagnosis and prognosis of mental disorders
because it provides brain biomarkers. However, only highly trained doctors can interpret …
because it provides brain biomarkers. However, only highly trained doctors can interpret …
EEG-based intention recognition with deep recurrent-convolution neural network: Performance and channel selection by Grad-CAM
Y Li, H Yang, J Li, D Chen, M Du - Neurocomputing, 2020 - Elsevier
Abstract Electroencephalography (EEG) based Brain-Computer Interface (BCI) enables
subjects to communicate with the outside world or control equipment using brain signals …
subjects to communicate with the outside world or control equipment using brain signals …
Interpretable and lightweight convolutional neural network for EEG decoding: Application to movement execution and imagination
Convolutional neural networks (CNNs) are emerging as powerful tools for EEG decoding:
these techniques, by automatically learning relevant features for class discrimination …
these techniques, by automatically learning relevant features for class discrimination …
[HTML][HTML] An explainable deep-learning model to stage sleep states in children and propose novel EEG-related patterns in sleep apnea
Automatic deep-learning models used for sleep scoring in children with obstructive sleep
apnea (OSA) are perceived as black boxes, limiting their implementation in clinical settings …
apnea (OSA) are perceived as black boxes, limiting their implementation in clinical settings …
Accurate age classification using manual method and deep convolutional neural network based on orthopantomogram images
YC Guo, M Han, Y Chi, H Long, D Zhang… - International journal of …, 2021 - Springer
Age estimation is an important challenge in many fields, including immigrant identification,
legal requirements, and clinical treatments. Deep learning techniques have been applied for …
legal requirements, and clinical treatments. Deep learning techniques have been applied for …