Deep learning for medical anomaly detection–a survey
Machine learning–based medical anomaly detection is an important problem that has been
extensively studied. Numerous approaches have been proposed across various medical …
extensively studied. Numerous approaches have been proposed across various medical …
[HTML][HTML] Epileptic seizures detection using deep learning techniques: a review
A variety of screening approaches have been proposed to diagnose epileptic seizures,
using electroencephalography (EEG) and magnetic resonance imaging (MRI) modalities …
using electroencephalography (EEG) and magnetic resonance imaging (MRI) modalities …
Data augmentation for deep-learning-based electroencephalography
Background Data augmentation (DA) has recently been demonstrated to achieve
considerable performance gains for deep learning (DL)—increased accuracy and stability …
considerable performance gains for deep learning (DL)—increased accuracy and stability …
[HTML][HTML] Deep learning-based electroencephalography analysis: a systematic review
Context. Electroencephalography (EEG) is a complex signal and can require several years
of training, as well as advanced signal processing and feature extraction methodologies to …
of training, as well as advanced signal processing and feature extraction methodologies to …
Deep Learning for EEG motor imagery classification based on multi-layer CNNs feature fusion
Electroencephalography (EEG) motor imagery (MI) signals have recently gained a lot of
attention as these signals encode a person's intent of performing an action. Researchers …
attention as these signals encode a person's intent of performing an action. Researchers …
EEG based multi-class seizure type classification using convolutional neural network and transfer learning
Recognition of epileptic seizure type is essential for the neurosurgeon to understand the
cortical connectivity of the brain. Though automated early recognition of seizures from …
cortical connectivity of the brain. Though automated early recognition of seizures from …
Opportunities and challenges in developing deep learning models using electronic health records data: a systematic review
Objective To conduct a systematic review of deep learning models for electronic health
record (EHR) data, and illustrate various deep learning architectures for analyzing different …
record (EHR) data, and illustrate various deep learning architectures for analyzing different …
Deep learning with convolutional neural networks for EEG decoding and visualization
RT Schirrmeister, JT Springenberg… - Human brain …, 2017 - Wiley Online Library
Deep learning with convolutional neural networks (deep ConvNets) has revolutionized
computer vision through endtoend learning, that is, learning from the raw data. There is …
computer vision through endtoend learning, that is, learning from the raw data. There is …
EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces
VJ Lawhern, AJ Solon, NR Waytowich… - Journal of neural …, 2018 - iopscience.iop.org
Objective. Brain–computer interfaces (BCI) enable direct communication with a computer,
using neural activity as the control signal. This neural signal is generally chosen from a …
using neural activity as the control signal. This neural signal is generally chosen from a …
A long short-term memory deep learning network for the prediction of epileptic seizures using EEG signals
The electroencephalogram (EEG) is the most prominent means to study epilepsy and
capture changes in electrical brain activity that could declare an imminent seizure. In this …
capture changes in electrical brain activity that could declare an imminent seizure. In this …