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Seizures

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A Multimodal AI System for Out-of-Distribution Generalization of Seizure Identification.

IEEE journal of biomedical and health informatics
Artificial intelligence (AI) and health sensory data-fusion hold the potential to automate many laborious and time-consuming processes in hospitals or ambulatory settings, e.g. home monitoring and telehealth. One such unmet challenge is rapid and acc...

A method for AI assisted human interpretation of neonatal EEG.

Scientific reports
The study proposes a novel method to empower healthcare professionals to interact and leverage AI decision support in an intuitive manner using auditory senses. The method's suitability is assessed through acoustic detection of the presence of neonat...

EEG-Based Epileptic Seizure Detection via Machine/Deep Learning Approaches: A Systematic Review.

Computational intelligence and neuroscience
Epileptic seizure is one of the most chronic neurological diseases that instantaneously disrupts the lifestyle of affected individuals. Toward developing novel and efficient technology for epileptic seizure management, recent diagnostic approaches ha...

Deep-learning-based seizure detection and prediction from electroencephalography signals.

International journal for numerical methods in biomedical engineering
Electroencephalography (EEG) is among the main tools used for analyzing and diagnosing epilepsy. The manual analysis of EEG must be conducted by highly trained clinicians or neuro-physiologists; a process that is considered to have a comparatively lo...

Deep learning resting state functional magnetic resonance imaging lateralization of temporal lobe epilepsy.

Epilepsia
OBJECTIVE: Localization of focal epilepsy is critical for surgical treatment of refractory seizures. There remains a great need for noninvasive techniques to localize seizures for surgical decision-making. We investigate the use of deep learning usin...

Both Cross-Patient and Patient-Specific Seizure Detection Based on Self-Organizing Fuzzy Logic.

International journal of neural systems
Automatic epilepsy detection is of great significance for the diagnosis and treatment of patients. Most detection methods are based on patient-specific models and have achieved good results. However, in practice, new patients do not have their own pr...

Prediction Value of Epilepsy Secondary to Inferior Cavity Hemorrhage Based on Scalp EEG Wave Pattern in Deep Learning.

Journal of healthcare engineering
OBJECTIVE: To search the predictive value of epilepsy secondary to acute subarachnoid hemorrhage (aSAH) based on EEG wave pattern in deep learning.

An interactive framework for the detection of ictal and interictal activities: Cross-species and stand-alone implementation.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Despite advances on signal analysis and artificial intelligence, visual inspection is the gold standard in event detection on electroencephalographic recordings. This process requires much time of clinical experts on both an...

Automated seizure activity tracking and onset zone localization from scalp EEG using deep neural networks.

PloS one
We propose a novel neural network architecture, SZTrack, to detect and track the spatio-temporal propagation of seizure activity in multichannel EEG. SZTrack combines a convolutional neural network encoder operating on individual EEG channels with re...

Characterizing Brain Signals for Epileptic Pre-ictal Signal Classification.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Epilepsy is a kind of neurological disorder characterized by recurrent epileptic seizures. While it is crucial to characterize pre-ictal brain electrical activities, the problem to this day still remains computationally challenging. Using brain signa...