AIMC Topic: Epilepsies, Partial
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Deep learning-based automated detection and multiclass classification of focal interictal epileptiform discharges in scalp electroencephalograms.
Scientific reports
Apr 25, 2023
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 neuromorphic spiking neural network detects epileptic high frequency oscillations in the scalp EEG.
Scientific reports
Feb 2, 2022
Interictal High Frequency Oscillations (HFO) are measurable in scalp EEG. This development has aroused interest in investigating their potential as biomarkers of epileptogenesis, seizure propensity, disease severity, and treatment response. The deman...
Signal Processing, Computer-Assisted
Artifacts
Brain Waves
Drug Resistant Epilepsy
Treatment Outcome
Predictive Value of Tests
Male
Electroencephalography
Infant
Epilepsies, Partial
Adolescent
Neural Networks, Computer
Wearable Electronic Devices
Humans
Brain
Time Factors
Female
Child, Preschool
Reproducibility of Results
Child
Electroclinical spectrum of generalized paroxysmal fast activity in adults without epileptic encephalopathy.
Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
Jan 16, 2022
INTRODUCTION: Generalized paroxysmal fast activity (GPFA) is a rare and underreported EEG pattern known to be related to epileptic encephalopathy. We aimed to investigate the electroclinical spectrum of GPFA along with other atypical EEG features in ...
Fine motor impairment in children with epilepsy: Relations with seizure severity and lateralizing value.
Epilepsy & behavior : E&B
Jan 8, 2022
Motor skill deficits are common in epilepsy. The Grooved Pegboard Test (GPT) is the most commonly used fine motor task and is included in the NIH Common Data Elements Battery for the assessment of epilepsy. However, there are limited data on its util...
Can we predict anti-seizure medication response in focal epilepsy using machine learning?
Clinical neurology and neurosurgery
Nov 16, 2021
OBJECTIVE: The aim of this study was to evaluate the feasibility of machine learning approach based on clinical factors and diffusion tensor imaging (DTI) to predict anti-seizure medication (ASM) response in focal epilepsy. We hypothesized that ASM r...
Mapping Epileptogenic Tissues in MRI-Negative Focal Epilepsy: Can Deep Learning Uncover Hidden Lesions?
Neurology
Sep 14, 2021
Identification of focal epilepsy by diffusion tensor imaging using machine learning.
Acta neurologica Scandinavica
Mar 18, 2021
OBJECTIVE: The aim of this study was to evaluate the feasibility of machine learning based on diffusion tensor imaging (DTI) measures to distinguish patients with focal epilepsy versus healthy controls and antiseizure medication (ASM) responsiveness.
Prediction of baseline expressive and receptive language function in children with focal epilepsy using diffusion tractography-based deep learning network.
Epilepsy & behavior : E&B
Mar 16, 2021
PURPOSE: Focal epilepsy is a risk factor for language impairment in children. We investigated whether the current state-of-the-art deep learning network on diffusion tractography connectome can accurately predict expressive and receptive language sco...
Accurate detection of spontaneous seizures using a generalized linear model with external validation.
Epilepsia
Aug 6, 2020
OBJECTIVE: Seizure detection is a major facet of electroencephalography (EEG) analysis in neurocritical care, epilepsy diagnosis and management, and the instantiation of novel therapies such as closed-loop stimulation or optogenetic control of seizur...
Macroscale and microcircuit dissociation of focal and generalized human epilepsies.
Communications biology
May 18, 2020
Thalamo-cortical pathology plays key roles in both generalized and focal epilepsies, but there is little work directly comparing these syndromes at the level of whole-brain mechanisms. Using multimodal imaging, connectomics, and computational simulat...
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