New Smart Helmet Rapidly Assesses Stroke Patients

It uses EM waves to distinguish the size, position, and type of stroke

3 min read
Back view of a man's head. A large green object encircles it, with wires coming out of white rectangular shapes across the surface.

The proposed helmet uses electromagnetic waves to estimate the size and position of stroke inside a patient's brain.

FOS S.p.A./University of Genoa

This article is part of our exclusive IEEE Journal Watch series in partnership with IEEE Xplore.

When someone experiences a stroke, every passing moment leading up to treatment is critical. Ideally, patients should be diagnosed and treated within the first hour, often referred to as "the Golden Hour," in order to have the best chance at recovery. Given such a tight timeline, numerous research teams have been developing portable smart helmets for diagnosing stroke in patients as they are being transported to the hospital, rather than waiting until the patient arrives at the hospital to begin testing.

Many of the smart helmet designs being explored rely on ultrasound to image the brain and detect stroke; however, this approach has several downfalls. "Ultrasound usually requires skilled personnel in order to correctly interpret the resulting images," explains Alessandro Fedeli is an assistant professor in the Department of Electrical, Electronic, Telecommunications Engineering, and Naval Architecture, at the University of Genoa. He also notes that ultrasound doesn't penetrate the skull as well as, say, electromagnetic (EM) waves.

For these reasons, his team sought to create a smart helmet that relies on EM waves, along with a signal-processing approach, to detect and diagnose stroke. Notably, EM measurements are particularly helpful in the context of diagnosing stroke because the two types of stroke—ischemic and hemorrhagic—have different dielectric properties, which can be detected by EM. This allows the device developed by Fedeli and his colleagues to not only confirm the presence of stroke, but also determine what kind occurred. This more detailed information is useful, given that ischemic and hemorrhagic strokes require different treatment.

The helmet is made up of numerous antennas that are selectively activated to direct EM waves throughout the brain, and the returning signals are measured. A simple EM signal-processing algorithm alerts paramedics and other health professionals whether or not a stroke has occurred. A more complex algorithm, which requires more computational power, can then be used to determine the type, size and position of stroke.

The researchers tested their prototype through simulations that considered various stroke positions and dimensions (between 1 and 4 centimeters) inside the brain. They describe their results in a study published July 19 in IEEE Wireless Communications.

"With the signal processing approach, the overall accuracy is above 80 percent, which represents an interesting and an encouraging starting point," says Fedeli. "Concerning the EM approach, the results show that it is possible to create quite accurate images of the stroke inside the brain, and gain quantitative information about [the type of stroke]."

His team is hoping to test their design in a clinical trial in the near future. It will be interesting to see how theirs compares to a portable design by researchers at Yale University. This latter design, which relies on MRI data to diagnose stroke in the hospital setting, was tested in 144 patients and found to have 80 percent accuracy.

But cost and portability outside of the hospital setting are important factors. "It is worth noting that EM systems work in the microwave frequency band, similar to other widespread wireless apparatuses," says Fedeli. "Consequently, it is possible to realize compact, portable and rather unexpensive devices that can be used in the ambulance or at a patient's home."

But first, clinical trials are in order. "We hope to be able to perform clinical trials in the near future, possibly in cooperation with local hospitals," says Fedeli. "We also know that there are several other EM-based systems for stroke detection proposed by other research teams that have been already validated or are in course of validation with clinical trials, with positive and promising results."

This article appears in the October 2021 print issue as "Smart Helmet Provides Early Stroke Diagnosis."

The Conversation (0)
Illustration showing an astronaut performing mechanical repairs to a satellite uses two extra mechanical arms that project from a backpack.

Extra limbs, controlled by wearable electrode patches that read and interpret neural signals from the user, could have innumerable uses, such as assisting on spacewalk missions to repair satellites.

Chris Philpot

What could you do with an extra limb? Consider a surgeon performing a delicate operation, one that needs her expertise and steady hands—all three of them. As her two biological hands manipulate surgical instruments, a third robotic limb that’s attached to her torso plays a supporting role. Or picture a construction worker who is thankful for his extra robotic hand as it braces the heavy beam he’s fastening into place with his other two hands. Imagine wearing an exoskeleton that would let you handle multiple objects simultaneously, like Spiderman’s Dr. Octopus. Or contemplate the out-there music a composer could write for a pianist who has 12 fingers to spread across the keyboard.

Such scenarios may seem like science fiction, but recent progress in robotics and neuroscience makes extra robotic limbs conceivable with today’s technology. Our research groups at Imperial College London and the University of Freiburg, in Germany, together with partners in the European project NIMA, are now working to figure out whether such augmentation can be realized in practice to extend human abilities. The main questions we’re tackling involve both neuroscience and neurotechnology: Is the human brain capable of controlling additional body parts as effectively as it controls biological parts? And if so, what neural signals can be used for this control?

Keep Reading ↓Show less
{"imageShortcodeIds":[]}