AIMC Topic: Epilepsy

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The promise of AI Large Language Models for Epilepsy care.

Epilepsy & behavior : E&B
Artificial intelligence (AI) has been supporting our digital life for decades, but public interest in this has exploded with the recognition of large language models, such as GPT-4. We examine and evaluate the potential uses for generative AI technol...

eDeeplepsy: An artificial neural framework to reveal different brain states in children with epileptic spasms.

Epilepsy & behavior : E&B
OBJECTIVE: Despite advances, analysis and interpretation of EEG still essentially rely on visual inspection by a super-specialized physician. Considering the vast amount of data that composes the EEG, much of the detail inevitably escapes ordinary hu...

A comparative study of CNN-capsule-net, CNN-transformer encoder, and Traditional machine learning algorithms to classify epileptic seizure.

BMC medical informatics and decision making
INTRODUCTION: Epilepsy is a disease characterized by an excessive discharge in neurons generally provoked without any external stimulus, known as convulsions. About 2 million people are diagnosed each year in the world. This process is carried out by...

Timing matters for accurate identification of the epileptogenic zone.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
OBJECTIVE: Interictal biomarkers of the epileptogenic zone (EZ) and their use in machine learning models open promising avenues for improvement of epilepsy surgery evaluation. Currently, most studies restrict their analysis to short segments of intra...

Classification of self-limited epilepsy with centrotemporal spikes by classical machine learning and deep learning based on electroencephalogram data.

Brain research
Electroencephalogram (EEG) has been widely utilized as a valuable assessment tool for diagnosing epilepsy in hospital settings. However, clinical diagnosis of patients with self-limited epilepsy with centrotemporal spikes (SeLECTS) is challenging due...

Frame-based versus robot-assisted stereo-electro-encephalography for drug-resistant epilepsy.

Acta neurochirurgica
BACKGROUND: Stereoelectroencephalography (SEEG) is an effective presurgical invasive evaluation for drug-resistant epilepsies. The introduction of robotic devices provides a simplified, accurate, and safe alternative to the conventional SEEG techniqu...

Potential merits and flaws of large language models in epilepsy care: A critical review.

Epilepsia
The current pace of development and applications of large language models (LLMs) is unprecedented and will impact future medical care significantly. In this critical review, we provide the background to better understand these novel artificial intell...

Robotic assessment of sensorimotor and cognitive deficits in patients with temporal lobe epilepsy.

Epilepsy & behavior : E&B
OBJECTIVE: Individuals with temporal lobe epilepsy (TLE) frequently demonstrate impairments in executive function, working memory, and/or declarative memory. It is recommended that screening for cognitive impairment is undertaken in all people newly ...

Using Explainable Artificial Intelligence to Obtain Efficient Seizure-Detection Models Based on Electroencephalography Signals.

Sensors (Basel, Switzerland)
Epilepsy is a condition that affects 50 million individuals globally, significantly impacting their quality of life. Epileptic seizures, a transient occurrence, are characterized by a spectrum of manifestations, including alterations in motor functio...

Electrophysiological brain imaging based on simulation-driven deep learning in the context of epilepsy.

NeuroImage
Identifying the location, the spatial extent and the electrical activity of distributed brain sources in the context of epilepsy through ElectroEncephaloGraphy (EEG) recordings is a challenging task because of the highly ill-posed nature of the under...