AIMC Topic: Drug Resistant Epilepsy

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An Innovative Method for Refractory Epilepsy Diagnosis Based on Microstate Analysis and Graph Convolutional Network.

Journal of medical systems
This study systematically investigates the alterations in electroencephalogram (EEG) microstates in patients with refractory epilepsy(RE) across different seizure stages. A novel EEG microstate analysis framework is proposed to address the limitation...

PyHFO 2.0: an open-source platform for deep learning-based clinical high-frequency oscillations analysis.

Journal of neural engineering
Accurate detection and classification of high-frequency oscillations (HFOs) in electroencephalography (EEG) recordings have become increasingly important for identifying epileptogenic zones in patients with drug-resistant epilepsy. However, few open-...

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

BMJ open
INTRODUCTION: Epilepsy is a prevalent chronic neurological disorder, with approximately one-third of patients experiencing intractable epilepsy, often necessitating surgical intervention. Deep brain stimulation (DBS) of the thalamus has been introduc...

Clinical Value of ChatGPT for Epilepsy Presurgical Decision-Making: Systematic Evaluation of Seizure Semiology Interpretation.

Journal of medical Internet research
BACKGROUND: For patients with drug-resistant focal epilepsy, surgical resection of the epileptogenic zone (EZ) is an effective treatment to control seizures. Accurate localization of the EZ is crucial and is typically achieved through comprehensive p...

Deep learning-based EEG source imaging is robust under varying electrode configurations.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
OBJECTIVES: Previous research has underscored the necessity of high-density EEG for accurate and reliable EEG source imaging (ESI) results with conventional ESI methods, limiting their utility in clinical settings with only low-density EEG available....

Prediction of Pharmacoresistance in Drug-Naïve Temporal Lobe Epilepsy Using Ictal EEGs Based on Convolutional Neural Network.

Neuroscience bulletin
Approximately 30%-40% of epilepsy patients do not respond well to adequate anti-seizure medications (ASMs), a condition known as pharmacoresistant epilepsy. The management of pharmacoresistant epilepsy remains an intractable issue in the clinic. Its ...

Machine learning-based algorithm of drug-resistant prediction in newly diagnosed patients with temporal lobe epilepsy.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
OBJECTIVES: To develop a predicted algorithm for drug-resistant epilepsy (DRE) in newly diagnosed temporal lobe epilepsy (TLE) patients.

Deep Learning-Based Tract Classification of Preoperative DWI Tractography Advances the Prediction of Short-Term Postoperative Language Improvement in Children With Drug-Resistant Epilepsy.

IEEE transactions on bio-medical engineering
OBJECTIVE: To develop an innovative deep convolutional neural network (DCNN)-based tract classification to enhance the prediction of short-term postoperative language improvement using axonal connectivity markers derived from specific language modula...

Metrics for evaluation of automatic epileptogenic zone localization in intracranial electrophysiology.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
INTRODUCTION: Precise localization of the epileptogenic zone is critical for successful epilepsy surgery. However, imbalanced datasets in terms of epileptic vs. normal electrode contacts and a lack of standardized evaluation guidelines hinder the con...