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Epilepsy

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A hybrid 1D CNN-BiLSTM model for epileptic seizure detection using multichannel EEG feature fusion.

Biomedical physics & engineering express
Epilepsy, a chronic non-communicable disease is characterized by repeated unprovoked seizures, which are transient episodes of abnormal electrical activity in the brain. While Electroencephalography (EEG) is considered as the gold standard for diagno...

Automatic Detection of Scalp High-Frequency Oscillations Based on Deep Learning.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Scalp high-frequency oscillations (sHFOs) are a promising non-invasive biomarker of epilepsy. However, the visual marking of sHFOs is a time-consuming and subjective process, existing automatic detectors based on single-dimensional analysis have diff...

Artificial intelligence/machine learning for epilepsy and seizure diagnosis.

Epilepsy & behavior : E&B
Accurate seizure and epilepsy diagnosis remains a challenging task due to the complexity and variability of manifestations, which can lead to delayed or missed diagnosis. Machine learning (ML) and artificial intelligence (AI) is a rapidly developing ...

vEpiNet: A multimodal interictal epileptiform discharge detection method based on video and electroencephalogram data.

Neural networks : the official journal of the International Neural Network Society
To enhance deep learning-based automated interictal epileptiform discharge (IED) detection, this study proposes a multimodal method, vEpiNet, that leverages video and electroencephalogram (EEG) data. Datasets comprise 24 931 IED (from 484 patients) a...

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...