AIMC Topic: Seizures

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Edge deep learning for neural implants: a case study of seizure detection and prediction.

Journal of neural engineering
Implanted devices providing real-time neural activity classification and control are increasingly used to treat neurological disorders, such as epilepsy and Parkinson's disease. Classification performance is critical to identifying brain states appro...

Robot-assisted stereoelectroencephalography electrode placement in twenty-three pediatric patients: a high-resolution analysis of individual lead placement time and accuracy at a single institution.

Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery
PURPOSE: We describe a detailed evaluation of predictors associated with individual lead placement efficiency and accuracy for 261 stereoelectroencephalography (sEEG) electrodes placed for epilepsy monitoring in twenty-three children at our instituti...

Evaluation of artificial intelligence systems for assisting neurologists with fast and accurate annotations of scalp electroencephalography data.

EBioMedicine
BACKGROUND: Assistive automatic seizure detection can empower human annotators to shorten patient monitoring data review times. We present a proof-of-concept for a seizure detection system that is sensitive, automated, patient-specific, and tunable t...

Semi-dilated convolutional neural networks for epileptic seizure prediction.

Neural networks : the official journal of the International Neural Network Society
Epilepsy is a neurological brain disorder that affects ∼75 million people worldwide. Predicting epileptic seizures holds great potential for improving the quality of life of people with epilepsy, but seizure prediction solely from the Electroencephal...

Machine learning algorithms to predict seizure due to acute tramadol poisoning.

Human & experimental toxicology
INTRODUCTION: This study was designed to develop and evaluate machine learning algorithms for predicting seizure due to acute tramadol poisoning, identifying high-risk patients and facilitating appropriate clinical decision-making.

Deep Learning for EEG Seizure Detection in Preterm Infants.

International journal of neural systems
EEG is the gold standard for seizure detection in the newborn infant, but EEG interpretation in the preterm group is particularly challenging; trained experts are scarce and the task of interpreting EEG in real-time is arduous. Preterm infants are re...

Machine Learning Techniques for Personalized Detection of Epileptic Events in Clinical Video Recordings.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Continuous patient monitoring is essential to achieve an effective and optimal patient treatment in the intensive care unit. In the specific case of epilepsy it is the only way to achieve a correct diagnosis and a subsequent optimal medication plan i...

Machine Learning for Predicting Epileptic Seizures Using EEG Signals: A Review.

IEEE reviews in biomedical engineering
With the advancement in artificial intelligence (AI) and machine learning (ML) techniques, researchers are striving towards employing these techniques for advancing clinical practice. One of the key objectives in healthcare is the early detection and...

Preictal state detection using prodromal symptoms: A machine learning approach.

Epilepsia
A reliable identification of a high-risk state for upcoming seizures may allow for preemptive treatment and improve the quality of patients' lives. We evaluated the ability of prodromal symptoms to predict preictal states using a machine learning (ML...

Detection of Epileptic Seizure Using Pretrained Deep Convolutional Neural Network and Transfer Learning.

European neurology
INTRODUCTION: The diagnosis of epilepsy takes a certain process, depending entirely on the attending physician. However, the human factor may cause erroneous diagnosis in the analysis of the EEG signal. In the past 2 decades, many advanced signal pro...