US EditionGlasgow Researchers Develop AI Headset Predicting Epilepsy Seizures Minutes Ahead
The headset predicts seizures with up to 95% accuracy, offering critical early warnings to over 630,000 UK epilepsy patients to reduce injuries and increase safety.
- A unique AI-powered headset that can predict epileptic seizures minutes before they occur has been developed by researchers in Scotland.
- The headset analyses brainwaves and heart functions to detect an impending seizure, allowing the wearer to take precautions or warn those around them.
- Professor Hadi Larijani, who leads the research team at Glasgow Caledonian University, said providing even a few minutes warning could be life changing.
13 Articles
13 Articles
The Northern Echo
‘Unique’ AI-powered headset that can predict epilepsy seizures developed
Scientists say it could drastically change the way the condition is managed.
The Independent
Scientists develop AI headset to predict epilepsy seizures
Scientists hope the device can help give patients more warning ahead of seizures and prevent potential injuries
Scientists in Scotland have unveiled a groundbreaking artificial intelligence-powered headset that predicts seizures before they occur. The wearable device, developed by researchers at Glasgow Caledonian University, promises to change the way epilepsy is managed, alerting patients to an impending seizure and giving them valuable time to react and protect themselves from potential injury. The device works by analyzing brain waves and heart functi…
AI-powered headset can predict epilepsy seizures before they occur – UK Times
Sign up for our free Health Check email to receive exclusive analysis on the week in health Get our free Health Check email Get our free Health Check email Scientists have developed a groundbreaking AI-powered headset that can predict epilepsy seizures before they occur. Researchers at Glasgow Caledonian University in Scotland said the first-of-its-kind wearable device could help transform the way the condition is managed by alerting patients of…
Coverage Details
Bias Distribution
- 56% of the sources lean Left
Factuality
To view factuality data please Upgrade to Premium
