AIMC Topic: Epilepsy

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Accuracy of Depth Electrodes is Not Time-Dependent in Robot-Assisted Stereoelectroencephalography in a Pediatric Population.

Operative neurosurgery (Hagerstown, Md.)
BACKGROUND AND OBJECTIVES: Robot-assisted stereoelectroencephalography (sEEG) is steadily supplanting traditional frameless and frame-based modalities for minimally invasive depth electrode placement in epilepsy workup. Accuracy rates similar to gold...

Accuracy of robot-assisted stereotactic MRI-guided laser ablation in children with epilepsy.

Journal of neurosurgery. Pediatrics
OBJECTIVE: Robot-assisted (RA) stereotactic MRI-guided laser ablation has been reported to be a safe and effective technique for the treatment of epileptogenic foci in children and adults. In this study the authors aimed to assess the accuracy of RA ...

Long-term epilepsy outcome dynamics revealed by natural language processing of clinic notes.

Epilepsia
OBJECTIVE: Electronic medical records allow for retrospective clinical research with large patient cohorts. However, epilepsy outcomes are often contained in free text notes that are difficult to mine. We recently developed and validated novel natura...

Deep Learning With Convolutional Neural Networks for Motor Brain-Computer Interfaces Based on Stereo-Electroencephalography (SEEG).

IEEE journal of biomedical and health informatics
OBJECTIVE: Deep learning based on convolutional neural networks (CNN) has achieved success in brain-computer interfaces (BCIs) using scalp electroencephalography (EEG). However, the interpretation of the so-called 'black box' method and its applicati...

Neurophysiology, Neuropsychology, and Epilepsy, in 2022: Hills We Have Climbed and Hills Ahead. Neurophysiology in epilepsy.

Epilepsy & behavior : E&B
Since the discovery of the human electroencephalogram (EEG), neurophysiology techniques have become indispensable tools in our armamentarium to localize epileptic seizures. New signal analysis techniques and the prospects of artificial intelligence a...

Deep learning-based automated detection and multiclass classification of focal interictal epileptiform discharges in scalp electroencephalograms.

Scientific reports
Detection and spatial distribution analyses of interictal epileptiform discharges (IEDs) are important for diagnosing, classifying, and treating focal epilepsy. This study proposes deep learning-based models to detect focal IEDs in electroencephalogr...

A pharmacokinetic-pharmacodynamic model based on the SSA-1DCNN-Attention network and the semicompartment method.

Biotechnology & genetic engineering reviews
To solve the problem of inaccurate prediction caused by the lack of representativeness of samples due to the small sample size of the collected clinical data when using machine learning methods to predict drug concentration in plasma and describe the...

LightSeizureNet: A Lightweight Deep Learning Model for Real-Time Epileptic Seizure Detection.

IEEE journal of biomedical and health informatics
The monitoring of epilepsy patients in non-hospital environment is highly desirable, where ultra-low power wearable seizure detection devices are essential in such a system. The state-of-the-art epileptic seizure detection algorithms targeting such d...

From basic sciences and engineering to epileptology: A translational approach.

Epilepsia
Collaborative efforts between basic scientists, engineers, and clinicians are enabling translational epileptology. In this article, we summarize the recent advances presented at the International Conference for Technology and Analysis of Seizures (IC...

Discriminating and understanding brain states in children with epileptic spasms using deep learning and graph metrics analysis of brain connectivity.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Epilepsy is a brain disorder consisting of abnormal electrical discharges of neurons resulting in epileptic seizures. The nature and spatial distribution of these electrical signals make epilepsy a field for the analysis of ...