TR10: Personalized Medical Monitors
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The team is now working on a way to make that type of information useful to people with epilepsy. Today, many patients can control their seizures with an implant that stimulates the vagus nerve. The implant typically works in one of two ways: either it turns on every few minutes, regardless of a patient’s brain activity, or patients sweep a magnet over it, activating it when they sense a seizure coming on. Both methods have their drawbacks, so Guttag is designing a noninvasive, software-driven sensor programmed to measure the wearer’s brain waves and determine what patterns–specific to him or her–signify the onset of a seizure. Once those patterns are detected, a device can automatically activate an implant, stopping the seizure in its tracks.
Guttag plans to test the sensor, essentially a bathing cap of electrodes that fits over the scalp, on a handful of patients at Beth Israel Deaconess Medical Center this spring. Down the line, such a sensor could also help people without implants, simply warning them to sit down, pull over, or get to a safe place before a seizure begins. “Just a warning could be enormously life changing,” says Guttag. “It’s all the collateral damage that people really fear.”
Now he’s turned his attention to patterns of the heart. Like the brain, cardiac activity is governed by electrical signals, so moving into cardiology is a natural transition for Guttag.
He began by looking for areas where large-scale cardiac-data analysis was needed. Today, many patients who have suffered heart attacks go home with Holter monitors that record heart activity. After a day or so, a cardiologist reviews the monitor’s readings for worrisome signs. But it can be easy to miss an abnormal pattern in thousands of minutes of dense waveforms.
That’s where Guttag hopes computers can step in. Working with Collin Stultz, a cardiologist and assistant professor of electrical engineering and computer science at MIT, and graduate student Zeeshan Syed, Guttag is devising algorithms to analyze EKG readings for statistically meaningful patterns. In the coming months, the team will compare EKG records from hundreds of heart attack patients, some of whose attacks were fatal. The immediate goal is to pick out key similarities and differences between those who survived and those who didn’t. There are known “danger patterns” that physicians can spot on an EKG readout, but the Guttag group is leaving it up to the computer to find significant patterns, rather than telling it what to look for. If the computer’s search isn’t influenced by existing medical knowledge, Guttag reasons, it may uncover unexpected relationships.
Joseph Kannry, director of the Center for Medical Informatics at the Mount Sinai School of Medicine, calls Guttag’s work a solid step toward developing more accurate automated medical readings. “It’s promising. The challenge is going to be in convincing a clinician to use it,” says Kannry.
Still, Guttag feels he is well on his way toward integrating computing into medical diagnostics. “People have very different reactions when you tell them computers are going to make decisions for you,” he says. “But we’ve gotten to the point where computers fly our airplanes for us, so there’s every reason to be optimistic.”