The human body and the systems that maintain it are, at their most basic, bundles of crackling electricity. Impulses, currents and waves can be found in every part of our world, and they offer much in the way of information if they can be properly read and interpreted.
At the abstract level, Professor John Guttag and his research team are engaged in applied signal processing. But the marriage they have made between computer systems and medical research is vigorous and thriving. While it has already spawned impressive accomplishments, the most exciting opportunities to positively impact the practice of medicine lie in the team’s future.
When Guttag began working in the area of medical systems, he had no background in medicine. He was approached by a group of doctors who had grown frustrated with the limitations of current technology for diagnosis and treatment of cardiac patients. Cardiovascular disease is, at present, the leading cause of death worldwide. In the United States alone, nearly two million people a year will suffer acute coronary syndrome (ACS) of one sort or another. And after these events, there is no accurate way of predicting future outcomes. It is unknown who in that population is at high risk for recurrence and should be treated aggressively, and who would benefit most from something like a change in lifestyle and diet. The stakes for that uncertainty are high; some patients will go on to be perfectly healthy, and others will die — 5 percent of them in the first 90 days following the event.
In a partnership with cardiologists Ben Scirica (Brigham and Women’s) and Collin Stultz (VA Hospital, MIT), the researchers have analyzed EKG data from more than 6,000 patients who have suffered ACS, in search of micro-instabilities — invisible to the eye of even the most well-trained cardiologist. Their thesis is that these nearly unnoticeable irregularities, much like small seismic tremors, can predict future instabilities with greater adverse significance.To view the full story, please visit http://www.csail.mit.edu/csailspotlights/feature13