AIMC Topic: Seizures

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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...

Event driven neural network on a mixed signal neuromorphic processor for EEG based epileptic seizure detection.

Scientific reports
Long-term monitoring of biomedical signals is essential for the modern clinical management of neurological conditions such as epilepsy. However, developing wearable systems that are able to monitor, analyze, and detect epileptic seizures with long-la...

Retraining and evaluation of machine learning and deep learning models for seizure classification from EEG data.

Scientific reports
Electroencephalography (EEG) is one of the most used techniques to perform diagnosis of epilepsy. However, manual annotation of seizures in EEG data is a major time-consuming step in the analysis process of EEGs. Different machine learning models hav...

Tiny Convolutional Neural Network with Supervised Contrastive Learning for Epileptic Seizure Prediction.

International journal of neural systems
Automatic seizure prediction based on ElectroEncephaloGraphy (EEG) ensures the safety of patients with epilepsy and mitigates anxiety. In recent years, significant progress has been made in this field. However, the predictive performance of existing ...

FusionXNet: enhancing EEG-based seizure prediction with integrated convolutional and Transformer architectures.

Journal of neural engineering
. Effective seizure prediction can reduce patient burden, improve clinical treatment accuracy, and lower healthcare costs. However, existing deep learning-based seizure prediction methods primarily rely on single models, which have limitations in fea...

Initial seizure episodes risk factors identification during hospitalization of ICU patients: A retrospective analysis of the eICU collaborative research database.

Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia
BACKGROUND: We aimed to identify risk factors for initial seizure episodes in ICU patients using various machine learning algorithms.

Artificial intelligence for the detection of interictal epileptiform discharges in EEG signals.

Revue neurologique
INTRODUCTION: Over the past decades, the integration of modern technologies - such as electronic health records, cloud computing, and artificial intelligence (AI) - has revolutionized the collection, storage, and analysis of medical data in neurology...

Hardware Optimization and Implementation of a 16-Channel Neural Tree Classifier for On-Chip Closed-Loop Neuromodulation.

IEEE transactions on biomedical circuits and systems
This work presents the development of on-chip machine learning (ML) classifiers for implantable neuromodulation system-on-chips (SoCs), aimed at detecting epileptic seizures for closed-loop neuromodulation applications. Tree-based classifiers have ga...

Updates in Neonatal Seizures.

Clinics in perinatology
Neonatal seizures are a common medical emergency, necessitating prompt treatment. The most common etiologies include hypoxic-ischemic encephalopathy, ischemic stroke, and intracranial hemorrhage, with numerous other uncommon etiologies. Accurate diag...

Efficient Seizure Detection by Complementary Integration of Convolutional Neural Network and Vision Transformer.

International journal of neural systems
Epilepsy, as a prevalent neurological disorder, is characterized by its high incidence, sudden onset, and recurrent nature. The development of an accurate and real-time automatic seizure detection system is crucial for assisting clinicians in making ...