Seizures

Y-Site Compatibility of Intravenous Levetiracetam With Commonly Used Critical Care Medications.

Levetiracetam is an antiepileptic medication commonly used in critical care areas for seizure treatment or prophylaxis. Compatibility data of levetiracetam with other critical care medications are limited, which can make administration challenging. This study aims to assess the physical Y-site compatibility of intravenous levetiracetam with some other commonly used critical care medications. Y-s...

Classification of Depression Patients and Normal Subjects Based on Electroencephalogram (EEG) Signal Using Alpha Power and Theta Asymmetry.

Depression or Major Depressive Disorder (MDD) is a mental illness which negatively affects how a person thinks, acts or feels. MDD has become a major disease affecting millions of people presently. The diagnosis of depression is questionnaire based and is not based on any objective criteria. In this paper, feature extracted from EEG signal are used for the diagnosis of depression. Alpha, alpha1, a...

A Tunable-Q wavelet transform and quadruple symmetric pattern based EEG signal classification method.

Electroencephalography (EEG) signals have been widely used to diagnose brain diseases for instance epilepsy, Parkinson's Disease (PD), Multiple Sklero...

Perspectives on the current developments with neuromodulation for the treatment of epilepsy.

: As deep brain stimulation revolutionized the treatment of movement disorders in the late 80s, neuromodulation in the treatment of epilepsy will undo...

Robot-assisted versus manual navigated stereoelectroencephalography in adult medically-refractory epilepsy patients.

OBJECTIVE: Stereoelectroencephalography (SEEG) has experienced a recent growth in adoption for epileptogenic zone (EZ) localization. Advances in robot...

Machine learning: assessing neurovascular signals in the prefrontal cortex with non-invasive bimodal electro-optical neuroimaging in opiate addiction.

Chronic and recurrent opiate use injuries brain tissue and cause serious pathophysiological changes in hemodynamic and subsequent inflammatory respons...

Super-Resolution for Improving EEG Spatial Resolution using Deep Convolutional Neural Network-Feasibility Study.

Electroencephalography (EEG) has relatively poor spatial resolution and may yield incorrect brain dynamics and distort topography; thus, high-density ...

Deep Learning Approach for Epileptic Focus Localization.

The task of epileptic focus localization receives great attention due to its role in an effective epileptic surgery. The clinicians highly depend on t...

Neonatal seizure detection from raw multi-channel EEG using a fully convolutional architecture.

A deep learning classifier for detecting seizures in neonates is proposed. This architecture is designed to detect seizure events from raw electroence...

Prospective validation of a machine learning model that uses provider notes to identify candidates for resective epilepsy surgery.

OBJECTIVE: Delay to resective epilepsy surgery results in avoidable disease burden and increased risk of mortality. The objective was to prospectively...

Automated detection of hippocampal sclerosis using clinically empirical and radiomics features.

OBJECTIVE: Temporal lobe epilepsy is a common form of epilepsy that might be amenable to surgery. However, magnetic resonance imaging (MRI)-negative h...

Regularized siamese neural network for unsupervised outlier detection on brain multiparametric magnetic resonance imaging: Application to epilepsy lesion screening.

In this study, we propose a novel anomaly detection model targeting subtle brain lesions in multiparametric MRI. To compensate for the lack of annotat...

Use of deep learning to detect personalized spatial-frequency abnormalities in EEGs of children with ADHD.

OBJECTIVE: Attention-deficit/hyperactivity disorder (ADHD) is one of the most prevalent neurobehavioral disorders. Studies have tried to find the neur...

Semi-supervised Training Data Selection Improves Seizure Forecasting in Canines with Epilepsy.

OBJECTIVE: Conventional selection of pre-ictal EEG epochs for seizure prediction algorithm training data typically assumes a continuous pre-ictal brai...

Subject-Independent Brain-Computer Interfaces Based on Deep Convolutional Neural Networks.

For a brain-computer interface (BCI) system, a calibration procedure is required for each individual user before he/she can use the BCI. This procedur...

Automated detection of focal cortical dysplasia using a deep convolutional neural network.

Focal cortical dysplasia (FCD) is one of the commonest epileptogenic lesions, and is related to malformations of the cortical development. The finding...

Detection of mesial temporal lobe epileptiform discharges on intracranial electrodes using deep learning.

OBJECTIVE: Develop a high-performing algorithm to detect mesial temporal lobe (mTL) epileptiform discharges on intracranial electrode recordings.

Predicting individual decision-making responses based on single-trial EEG.

Decision-making plays an essential role in the interpersonal interactions and cognitive processing of individuals. There has been increasing interest ...

A Multi-Column CNN Model for Emotion Recognition from EEG Signals.

We present a multi-column CNN-based model for emotion recognition from EEG signals. Recently, a deep neural network is widely employed for extracting ...

A hierarchical sequential neural network with feature fusion for sleep staging based on EOG and RR signals.

OBJECTIVE: Currently, the automatic sleep staging methods mainly face two problems: the first problem is that although the algorithms which use electr...