We use cookies to understand how you use our site and to improve your experience. This includes personalizing content and advertising. To learn more, click here. By continuing to use our site, you accept our use of cookies. Cookie Policy.

Features Partner Sites Information LinkXpress
Sign In
Advertise with Us
Sekisui Diagnostics UK Ltd.

Download Mobile App




New AI Algorithm Detects Rare Epileptic Seizures from EEG Data

By HospiMedica International staff writers
Posted on 07 Jun 2024
Print article
Image: The new AI system accurately detects epileptic seizure types (Photo courtesy of 123RF)
Image: The new AI system accurately detects epileptic seizure types (Photo courtesy of 123RF)

Over 65 million people around the globe are affected by epilepsy, a neurological disorder that impacts the nervous system and causes seizures. Statistically, one in 26 individuals will experience epilepsy during their lifetime, and each year, 1 out of 1000 people with epilepsy die from unexpected deaths. Early detection is crucial for effective epilepsy treatment. Machine learning techniques have been employed to detect and classify seizures from electroencephalography (EEG) signals, which are captured using electrodes on the brain, identifying patterns too complex for human analysis alone. However, these systems have faced challenges in detecting rare forms of epileptic seizures due to their reliance on large data sets to learn patterns and make predictions, resulting in inadequate performance when encountering less common seizures. Researchers have now developed an advanced AI system capable of accurately detecting various types of epileptic seizures, thereby enhancing the diagnosis of rare and complex cases, even in young children.

The AI system, created by computer science researchers at the University of Southern California (Los Angeles, CA, USA), enhances the diagnosis of rare and complex epilepsy cases by analyzing brain interactions. This new system integrates multiple sources of information typically overlooked by AI systems in epilepsy detection, such as the positions of EEG electrodes and the brain regions they monitor. By doing so, the AI can identify patterns or features that signal an impending seizure. This approach enables the system to produce accurate results with minimal data, even for rare seizure types that have limited examples in the training data.

For example, in the case of atonic seizures—a rare type of seizure often affecting children and causing sudden loss of muscle control and collapse—the system focuses on spatial relationships in brain regions. It prioritizes brain areas involved in muscle control, such as the motor cortex, basal ganglia, cerebellum, and brainstem, to detect activity patterns indicative of atonic seizures. The researchers aim to supplement doctors' expertise in diagnosing difficult cases rather than replace them. They view this AI technology as a significant advancement in clinical neurology, with the potential to be integrated into wearable sensors that can relay information to a smartphone in the future.

“Brain seizures happen very suddenly, and so detecting seizures earlier really could save lives. The system could prompt an alert if it detects any irregularities in the brain waves. This would open up incredible opportunities for diagnosis and treatment of epilepsy,” said Cyrus Shahabi, a computer science, electrical engineering, and spatial sciences professor.

Related Links:
University of Southern California


Print article

Channels

Surgical Techniques

view channel
Image: Robotics and AI combination has potential to improve safety in emergency stroke surgeries (Photo courtesy of KCL)

Robotics Combined with AI Improves Safety in Emergency Stroke Surgeries

Cardiovascular diseases remain the leading cause of death throughout Europe, resulting in over 4 million fatalities annually. Mechanical thrombectomies (MT), which involve the surgical removal of blood... Read more

Patient Care

view channel
Image: The portable, handheld BeamClean technology inactivates pathogens on commonly touched surfaces in seconds (Photo courtesy of Freestyle Partners)

First-Of-Its-Kind Portable Germicidal Light Technology Disinfects High-Touch Clinical Surfaces in Seconds

Reducing healthcare-acquired infections (HAIs) remains a pressing issue within global healthcare systems. In the United States alone, 1.7 million patients contract HAIs annually, leading to approximately... Read more

Health IT

view channel
Image: First ever institution-specific model provides significant performance advantage over current population-derived models (Photo courtesy of Mount Sinai)

Machine Learning Model Improves Mortality Risk Prediction for Cardiac Surgery Patients

Machine learning algorithms have been deployed to create predictive models in various medical fields, with some demonstrating improved outcomes compared to their standard-of-care counterparts.... Read more

Point of Care

view channel
Image: POCT offers cost-effective, accessible, and immediate diagnostic solutions (Photo courtesy of Flinders University)

POCT for Infectious Diseases Delivers Laboratory Equivalent Pathology Results

On-site pathology tests for infectious diseases in rural and remote locations can achieve the same level of reliability and accuracy as those conducted in hospital laboratories, a recent study suggests.... Read more
Copyright © 2000-2024 Globetech Media. All rights reserved.