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 …
Beyond supervised learning for pervasive healthcare
The integration of machine/deep learning and sensing technologies is transforming
healthcare and medical practice. However, inherent limitations in healthcare data, namely …
healthcare and medical practice. However, inherent limitations in healthcare data, namely …
EEGWaveNet: Multiscale CNN-based spatiotemporal feature extraction for EEG seizure detection
The detection of seizures in epileptic patients via Electroencephalography (EEG) is an
essential key to medical treatment. With the advances in deep learning, many approaches …
essential key to medical treatment. With the advances in deep learning, many approaches …
Deep learning for patient-independent epileptic seizure prediction using scalp EEG signals
Epilepsy is one of the most prevalent neurological diseases among humans and can lead to
severe brain injuries, strokes, and brain tumors. Early detection of seizures can help to …
severe brain injuries, strokes, and brain tumors. Early detection of seizures can help to …
EEG-based seizure prediction via Transformer guided CNN
Recently, most seizure prediction methods mainly utilize pure CNN or Transformer model,
which cannot extract local and global features simultaneously. To this end, we propose an …
which cannot extract local and global features simultaneously. To this end, we propose an …
Deep learning based efficient epileptic seizure prediction with EEG channel optimization
A seizure is an unstable situation in epilepsy patients due to excessive electrical discharge
by brain cells. An efficient seizure prediction method is required to reduce the lifetime risk of …
by brain cells. An efficient seizure prediction method is required to reduce the lifetime risk of …
Spatio-temporal-spectral hierarchical graph convolutional network with semisupervised active learning for patient-specific seizure prediction
Graph theory analysis using electroencephalogram (EEG) signals is currently an advanced
technique for seizure prediction. Recent deep learning approaches, which fail to fully …
technique for seizure prediction. Recent deep learning approaches, which fail to fully …
Optimizing epileptic seizure recognition performance with feature scaling and dropout layers
Epilepsy is a widespread neurological disorder characterized by recurring seizures that
have a significant impact on individuals' lives. Accurately recognizing epileptic seizures is …
have a significant impact on individuals' lives. Accurately recognizing epileptic seizures is …
Patient-specific seizure prediction via adder network and supervised contrastive learning
Deep learning (DL) methods have been widely used in the field of seizure prediction from
electroencephalogram (EEG) in recent years. However, DL methods usually have numerous …
electroencephalogram (EEG) in recent years. However, DL methods usually have numerous …
Seizure prediction using directed transfer function and convolution neural network on intracranial EEG
Automatic seizure prediction promotes the development of closed-loop treatment system on
intractable epilepsy. In this study, by considering the specific information exchange between …
intractable epilepsy. In this study, by considering the specific information exchange between …