GAME CHANGER
An AI-Powered Stethoscope for the Brain
Josef Parvizi, MD, PhD
Professor of Neurology and Neurological Sciences
Director, Laboratory of Behavioral & Cognitive Neuroscience
By Liz Wollman
Neuroscientist Josef Parvizi had an epiphany while listening to a performance by the Kronos Quartet in 2008. As musicians transformed plasma waves recorded by NASA’s Voyager probes and other spacecraft into “Sun Rings,” a 10-movement composition for strings, Parvizi began to wonder what it would be like to hear brain activity if it could be rendered in sound.
At Stanford, Parvizi, a professor of neurology and neurological sciences, collaborated with music professor Chris Chafe to convert electrophysiological recordings of the brain activity of epilepsy patients into sounds. When they analyzed the sounds, Parvizi found that a healthy brain and a brain undergoing a seizure had distinct sonic signals. The next step was to design a device “that could sonify the brain activity in real time and let the user ‘listen’ to the brain, almost like the way we listen to the heart,” says Parvizi.
He called the device a “brain stethoscope” and saw its potential to improve health outcomes for patients who suffer brain seizures during health emergencies. “If a patient is critically ill and admitted to the ICU, let’s say after a motor vehicle accident, we monitor the heart, kidneys, the liver, but not the brain, the most important organ,” Parvizi says. “Many patients, as a result, may have silent seizures in the ICU that cause brain damage if undetected and untreated.”
The consequences, which can include death or severe neurological and cognitive disability, are often attributed to the accident itself rather than doctors’ delay or inability to identify the seizure.
In 2014, Parvizi co-founded the medical device company Ceribell and developed a prototype of the brain stethoscope, intended as an alternative to conventional electroencephalography (EEG), which records and measures a brain’s electrical activity using electrodes. Parvizi spoke about the device at Stanford’s Big Ideas in Medicine conference, co-sponsored by the Stanford Center on Longevity.
The Ceribell EEG includes a disposable, flexible headband and a pocket-sized, battery-operated recorder used to capture and wirelessly transmit EEG signals. Unlike a conventional EEG, Ceribell’s hardware is simple to use and can be applied within minutes by non-specialized providers. Data captured by the recorder is analyzed by a proprietary, AI-powered seizure detection algorithm. Though the company’s inception was centered on the sonification of brain waves, the current device relies on AI rather than sound.
Ceribell’s brain stethoscope received FDA approval in 2017 and is now used in hundreds of hospitals. The company, which went public in October 2024, estimates that there are about three million acute care patients each year who should be monitored for non-convulsive seizures.
In acute care settings, seizures are commonly triggered by conditions including brain tumors, traumatic brain injury, stroke, cardiac arrest and sepsis. Not all are accompanied by obvious signs. For 90 percent of critically ill patients in emergency departments and intensive care units, seizures don’t involve convulsions, according to Parvizi. Averting serious brain damage allows patients who survive a major health crisis to have a longer healthspan despite their injuries, he said.
And, because there isn’t an easy way to gain insight into the brain’s roughly 86 billion neurons, detecting and treating brain seizures in acute care settings has been limited by the cost and complexity of conventional EEG, which requires trained technicians and can take hours.
Ceribell’s next focus areas include catastrophic stroke, delirium and other brain conditions not caused by traumatic brain injury. The portable EEG has also allowed under-resourced hospitals, including those in lower-income and rural communities, to conduct brain scans in acute care settings. “In many poor hospitals or poor places where the brain specialist is missing, the AI algorithm will be offering specialist-level diagnostic information,” says Parvizi.
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