AIMC Topic: Epilepsy

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Fully-Automated Spike Detection and Dipole Analysis of Epileptic MEG Using Deep Learning.

IEEE transactions on medical imaging
Magnetoencephalography (MEG) is a useful tool for clinically evaluating the localization of interictal spikes. Neurophysiologists visually identify spikes from the MEG waveforms and estimate the equivalent current dipoles (ECD). However, presently, t...

Localizing seizure onset zones in surgical epilepsy with neurostimulation deep learning.

Journal of neurosurgery
OBJECTIVE: In drug-resistant temporal lobe epilepsy, automated tools for seizure onset zone (SOZ) localization that use brief interictal recordings could supplement presurgical evaluations and improve care. Thus, the authors sought to localize SOZs b...

An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future works.

Computers in biology and medicine
Epilepsy is a disorder of the brain denoted by frequent seizures. The symptoms of seizure include confusion, abnormal staring, and rapid, sudden, and uncontrollable hand movements. Epileptic seizure detection methods involve neurological exams, blood...

Deep learning-based automated segmentation of resection cavities on postsurgical epilepsy MRI.

NeuroImage. Clinical
Accurate segmentation of surgical resection sites is critical for clinical assessments and neuroimaging research applications, including resection extent determination, predictive modeling of surgery outcome, and masking image processing near resecti...

A deep learning framework for epileptic seizure detection based on neonatal EEG signals.

Scientific reports
Electroencephalogram (EEG) is one of the main diagnostic tests for epilepsy. The detection of epileptic activity is usually performed by a human expert and is based on finding specific patterns in the multi-channel electroencephalogram. This is a dif...

Development of a natural language processing algorithm to extract seizure types and frequencies from the electronic health record.

Seizure
OBJECTIVE: To develop a natural language processing (NLP) algorithm to abstract seizure types and frequencies from electronic health records (EHR).

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...