A crew from the Amsterdam College Medical Middle (UMC), working with first responders and hospitals on the area, have demonstrated how a rapid-fit dry-electrode electroencephalogram (EEG) cap will help to diagnose a stroke at the back of an ambulance — directing the affected person to probably the most acceptable hospital accordingly.
“Our analysis reveals that the brain-wave cap can acknowledge sufferers with giant ischemic stroke with nice accuracy,” Jonathan Coutinho, a neurologist at Amsterdam UMC and co-inventor of the swimming-cap-like hat. “This is superb information, as a result of the cap can in the end save lives by routing these sufferers on to the proper hospital.”
The at-the-scene diagnostic method invented by Coutinho and colleagues Wouter Potters and Henk Marquering is designed to be as easy to make use of as potential. Positioned on the pinnacle and strapped below the chin in seconds, without having to shave the affected person’s hair or apply conductive gels, the brain-wave cap permits first responders to take EEG mind exercise readings at the back of an ambulance — and that knowledge, the crew has confirmed, can detect ischemic strokes and even whether or not the blocked blood vessel is giant or small.
That latter level is important: if the blocked blood vessel is small, it may be handled utilizing blood thinners at any hospital; if it is giant, it requires surgical intervention in a specialist hospital — and seconds depend. “In terms of stroke, time is actually mind. The earlier we begin the proper therapy, the higher the result,” Coutinho explains. “If the prognosis is already clear within the ambulance, the affected person will be routed on to the proper hospital, which saves invaluable time.”
The crew’s work, which noticed exams carried out in 12 Dutch ambulances between 2018 and 2022 overlaying a complete of almost 400 sufferers with suspected strokes, has been printed within the journal Neurology below open-access phrases.
Amsterdam UMC has shaped a spin-off firm dubbed TrianecT to commercialize the expertise and develop an improved algorithm for higher detection of enormous strokes.