Stanford Algorithm Turns Silent Seizures Into Sound Waves to Increase Detection
Orange County, CA - March 23rd, 2018 -
Researchers at Stanford have developed a device that gives any medical professional the ability to listen to brain waves and confidently detect silent seizures- the neurological condition where patients have epileptic seizures without any of the associated bodily convulsions. Much like the physical stethoscopes a doctor uses to check the heart or lungs of a patient, the “brain stethoscope” is a device worn over a patient’s head that translates the brain’s electrical activity into sounds using a custom algorithm.
To create the algorithm, Stanford researchers worked with a specialist in computer music for several years. “This technology will enable nurses, medical students, and physicians themselves to actually assess their patient right there and they will be able to determine if the patient is having silent seizures,” says Josef Parvizi, a professor of neurology and neurological sciences at Stanford University.
The brain stethoscope will help solve a basic problem: many epileptic seizures go undetected and untreated. Physical side effects of seizures don’t always accompany the seizure itself, but the internal damage still occurs and can be catastrophic when prolonged.
“You might think that all seizures must cause some sort of convulsions, namely a patient who’s having a seizure must fall down and shake on the ground. But that’s actually not the case, especially in critically ill patients in the intensive care units,” says Parvizi, who is also a member of Stanford Bio-X, the Stanford Neurosciences Institute, and the Child Health Research Institute. “Close to 90 percent of those patients will have silent seizures.”
Parvizi partnered with Chris Chafe, a professor of music at Stanford and also a member of Bio-X and the Neurosciences Institute. Chafe has experience with this music concept: he has previously made music out of climate change data and the carbon dioxide generated by ripening tomatoes. Once Parvizi sent the silent seizure data to Chafe, they knew an algorithm was possible. “Once he sent me the files and I listened to them, I was literally in shock, because it was so intuitive,” Parvizi says. “You could hear the transition from non-seizure to seizure so easily, that I just basically picked up the phone and told Chris that we have something right here.”
To guarantee the algorithm’s success, Parvizi wanted to ensure all medical professionals could detect a seizure, not just those trained in epilepsy diagnosis. Parvizi enlisted medical student Kapil Gururangan and clinical assistant professor of neurology Babak Razavi to gather a team of volunteers. 34 medical students and 30 nurses listened to 84 brainwave samples, called electroencephalograms or EEGs, 32 of which included some sort of seizure reaction. Overall, medical students and nurses were 97 percent accurate in deciphering the seizure-like events from normal brain waves. By comparison, when medical students and nurses tried to correctly read an EEG, the group was only accurate 50 percent of the time.
Next, the team wants to figure out how physicians in the field will use this technology in their practice, and based on the early positive results, there is a high probability. The team published their findings on March 21 in the medical journal Epilepsia.
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Stanford Algorithm Turns Silent Seizures Into Sound Waves to Increase Detection Orange County, CA – March 23rd, 2018 – Researchers at Stanford have developed a device that gives any medical professional the ability to listen to brain waves and confidently detect silent seizures- the neurological condition where patients have epileptic seizures without any of the associated […]