Seizures

Automated classification of seizure onset pattern using intracranial electroencephalogram signal of non-human primates.

To develop and validate a machine learning framework for the classification of distinct seizure onset patterns using intracranial EEG (iEEG) recordings in a non-human primate (NHP) model of penicillin-induced seizures.iEEG data were collected from six NHPs, comprising 1, 496 frontal and 549 temporal lobe seizures. Seizure onset patterns were manually categorized into five types: Sharp Activity (5-...

A Comparative Study of Conventional and Tripolar EEG for High-Performance Reach-to-Grasp BCI Systems.

This study aims to enhance brain-computer interface (BCI) applications for individuals with motor impairments by comparing the effectiveness of noninvasive tripolar concentric ring electrode electroencephalography (tEEG) with conventional electroencephalography (EEG) technology. The goal is to determine which EEG technology is more effective in measuring and decoding different grasp-related neural...

Thalamic neural activity and epileptic network analysis using stereoelectroencephalography: a prospective study protocol.

INTRODUCTION: Epilepsy is a prevalent chronic neurological disorder, with approximately one-third of patients experiencing intractable epilepsy, often...

Dynamic alterations of SEEG characteristics during peri-ictal period and localization of seizure onset zone.

BACKGROUND: The evolution in peri-ictal period (from pre-ictal to ictal phase) of seizures contains abundant epileptogenic information, which aids in ...

Enhancing differentiation between unipolar and bipolar depression through integration of machine learning and electroencephalogram analysis.

To enhance the differentiation between unipolar depression (UPD) and bipolar depression (BPD), this study integrates machine learning and deep learnin...

Optimal graph representations and neural networks for multichannel time series data in seizure phase classification.

In recent years, several machine-learning (ML) solutions have been proposed to solve the problems of seizure detection, seizure phase classification, ...

Attention-driven deep learning framework for EEG analysis in ADHD detection.

Attention Deficit Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder that affects cognitive functions such as attention, impulse control, ...

Resting state EEG classifies developmental status in three-year-old children.

Monitoring cognitive development in early childhood enables detection of problems for timely intervention. However, currently recommended methods requ...

Wearable Epilepsy Seizure Detection on FPGA with Spiking Neural Networks.

The development of epilepsy monitoring solutions suitable for everyday use is a very challenging task, where different constraints should be combined,...

Requirement Analysis for Data-Driven Electroencephalography Seizure Monitoring Software to Enhance Quality and Decision Making in Digital Care Pathways for Epilepsy: A Feasibility Study from the Perspectives of Health Care Professionals.

BACKGROUND: Abnormal brain activity is the source of epileptic seizures, which can present a variety of symptoms and influence patients' quality of li...

Classification of epilepsy seizure types in pediatrics based on Turkish EEG reports.

This study focuses on the binary classification of pediatric epilepsy seizure types as focal or generalized using Turkish electroencephalography (EEG)...

Comprehensive duck DNA fingerprinting based on machine learning for breed identification.

Duck is one of the most widely distributed waterfowl in the world, with more than 6 billion of them farmed annually in the world, and has great econom...

Brain stimulation outcome prediction in Major Depressive Disorder by deep learning models using EEG representations.

Major Depressive Disorder (MDD) is known as a widespread illness and needs a timely treatment. The treatment procedure is currently based on the trial...

An adaptive frequency partitioning framework for epileptic seizure detection using TransseizNet.

OBJECTIVES: Epilepsy is a disorder causing repeated seizures because of unusual brain activity recorded using electroencephalography. Nevertheless, co...

Deep learning on brief interictal intracranial recordings can accurately characterize seizure onset zones.

OBJECTIVE: Epilepsy is a debilitating disorder affecting more than 50 million people worldwide, and one third of patients continue to have seizures de...

Neuronal network analysis during short time transcranial direct current stimulation with permanent EEG measurement in early childhood: a feasibility study.

Transcranial direct current stimulation (tDCS) as a non-invasive stimulation is still in the experimental stage for many psychiatric disorders even in...

Multiband EEG signatures decoded using machine learning for predicting rTMS treatment response in MDD.

BACKGROUND: Repetitive transcranial magnetic stimulation (rTMS) is a promising treatment for major depression disorder (MDD), particularly for treatme...

Neurostimulation for the Management of Epilepsy: Advances in Targeted Therapy.

BACKGROUND: Epilepsy is a multifaceted neurological disorder marked by seizures that can present with a wide range of symptoms. Despite the prevalent ...

iSeizdiag: toward the framework development of epileptic seizure detection for healthcare.

INTRODUCTION: The seizure episodes result from abnormal and excessive electrical discharges by a group of brain cells. EEG framework-based signal acqu...

Enhanced visibility graph for EEG classification.

Electroencephalography (EEG) holds immense potential for decoding complex brain patterns associated with cognitive states and neurological conditions....