Seizures

Efficient use of clinical EEG data for deep learning in epilepsy.

OBJECTIVE: Automating detection of Interictal Epileptiform Discharges (IEDs) in electroencephalogram (EEG) recordings can reduce the time spent on visual analysis for the diagnosis of epilepsy. Deep learning has shown potential for this purpose, but the scarceness of expert annotated data creates a bottleneck in the process.

Identification of focal epilepsy by diffusion tensor imaging using machine learning.

OBJECTIVE: The aim of this study was to evaluate the feasibility of machine learning based on diffusion tensor imaging (DTI) measures to distinguish patients with focal epilepsy versus healthy controls and antiseizure medication (ASM) responsiveness.

Evaluation of artificial intelligence systems for assisting neurologists with fast and accurate annotations of scalp electroencephalography data.

BACKGROUND: Assistive automatic seizure detection can empower human annotators to shorten patient monitoring data review times. We present a proof-of-...

Semi-dilated convolutional neural networks for epileptic seizure prediction.

Epilepsy is a neurological brain disorder that affects ∼75 million people worldwide. Predicting epileptic seizures holds great potential for improving...

Multitask Feature Learning Meets Robust Tensor Decomposition for EEG Classification.

In this article, we study a tensor-based multitask learning (MTL) method for classification. Taking into account the fact that in many real-world appl...

Prediction of baseline expressive and receptive language function in children with focal epilepsy using diffusion tractography-based deep learning network.

PURPOSE: Focal epilepsy is a risk factor for language impairment in children. We investigated whether the current state-of-the-art deep learning netwo...

A machine learning approach to personalized dose adjustment of lamotrigine using noninvasive clinical parameters.

The pharmacokinetic variability of lamotrigine (LTG) plays a significant role in its dosing requirements. Our goal here was to use noninvasive clinica...

Effects of Robotic Neurorehabilitation on Body Representation in Individuals with Stroke: A Preliminary Study Focusing on an EEG-Based Approach.

Patients with stroke can experience a drastic change in their body representation (BR), beyond the physical and psychological consequences of stroke i...

External validation of automated focal cortical dysplasia detection using morphometric analysis.

OBJECTIVE: Focal cortical dysplasias (FCDs) are a common cause of drug-resistant focal epilepsy but frequently remain undetected by conventional magne...

Prediction of cerebral perfusion pressure during CPR using electroencephalogram in a swine model of ventricular fibrillation.

BACKGROUND: Measuring the quality of cardiopulmonary resuscitation (CPR) is important for improving outcomes in cardiac arrest. Cerebral perfusion pre...

A study on CNN image classification of EEG signals represented in 2D and 3D.

The novelty of this study consists of the exploration of multiple new approaches of data pre-processing of brainwave signals, wherein statistical feat...

Hybrid manifold-deep convolutional neural network for sleep staging.

Analysis of electroencephalogram (EEG) is a crucial diagnostic criterion for many sleep disorders, of which sleep staging is an important component. M...

Machine learning for automated EEG-based biomarkers of cognitive impairment during Deep Brain Stimulation screening in patients with Parkinson's Disease.

OBJECTIVE: A downside of Deep Brain Stimulation (DBS) for Parkinson's Disease (PD) is that cognitive function may deteriorate postoperatively. Electro...

Motor imagery recognition with automatic EEG channel selection and deep learning.

Modern motor imagery (MI)-based brain computer interface systems often entail a large number of electroencephalogram (EEG) recording channels. However...

Multi-Scale Frequency Bands Ensemble Learning for EEG-Based Emotion Recognition.

Emotion recognition has a wide range of potential applications in the real world. Among the emotion recognition data sources, electroencephalography (...

How hot is the hot zone? Computational modelling clarifies the role of parietal and frontoparietal connectivity during anaesthetic-induced loss of consciousness.

In recent years, specific cortical networks have been proposed to be crucial for sustaining consciousness, including the posterior hot zone and fronto...

Use of artificial intelligence on Electroencephalogram (EEG) waveforms to predict failure in early school grades in children from a rural cohort in Pakistan.

Universal primary education is critical for individual academic growth and overall adult productivity of nations. Estimates indicate that 25% of 59 mi...

SpikeDeep-classifier: a deep-learning based fully automatic offline spike sorting algorithm.

Advancements in electrode design have resulted in micro-electrode arrays with hundreds of channels for single cell recordings. In the resulting electr...

Machine learning algorithms to predict seizure due to acute tramadol poisoning.

INTRODUCTION: This study was designed to develop and evaluate machine learning algorithms for predicting seizure due to acute tramadol poisoning, iden...

Deep Representation-Based Domain Adaptation for Nonstationary EEG Classification.

In the context of motor imagery, electroencephalography (EEG) data vary from subject to subject such that the performance of a classifier trained on d...