You’re working in your house, going about your normal routine when suddenly the pain hits. Your chest starts to throb and your left arm begins to ache. Without hesitation, you rush to the hospital, dreading your worst fear has become a reality — you are having a heart attack. Upon arrival, physicians, nurses, and other medical staff begin frantically testing, probing, and prodding nearly every part of your body. They run more tests than you can keep track of and begin shouting orders for new tests and other members of the team. The emergency physician is carefully watching the monitors hooked up by your bedside, puzzled by the results they are seeing. They turn to consult a cardiologist expert on the signals your heart is emitting. But instead of a person, they turn to a computer.
Heart attacks and heart attack detection
Every day in the United States more than 2,000 people have heart attacks. Of these, over 400 people do not receive treatment in time. A heart attack occurs when material clogs the arteries that supply blood to the heart. Without blood, the heart does not have the necessary nutrients to continue functioning normally, and it begins to die. The longer a patient waits, the more likely the attack will cause irreparable damage to the heart. While researchers have made advancements in heart attack detection, the underlying methods remain unchanged from a century ago. Currently, physicians use the same electrocardiograms (ECGs) developed in the early 20th century to monitor the electrical activity of the heart. Depending on the location and severity of the heart attack, certain regions of the ECG may change. However, these changes are small, unreliable, and only include a small portion of the entire electrical signal of the heart.
Researchers have applied different signal processing and other complicated mathematical manipulations to ECGs. These processing steps have been unable to compensate for the differences in each heart in each person.
Just like your fingerprint, your heart has a slightly different shape, a different beating force, and thus, a different resting ECG signal than any other. Not to mention, the space between the heart and the recording device on the body’s surface can vary greatly with weight, gender, and overall body type. These variations make it very difficult for automated systems to predict what your specific heart is going through at any given moment. This necessitates a new system that can adapt based on your unique heart shape and signal to detect whether or not you are having a heart attack.
To improve electrocardiogram measurement techniques, our team utilized recent developments in computer science to “teach” computers to read cardiac electrical signals. With the incorporation of machine learning, electrocardiograms can tell us more than ever before about your heart.
How machine learning works
Machine learning is a technique developed by researchers to teach computers to identify unique features in datasets that are not easily distinguishable by the naked eye. Researchers give the computer multiple sets of categorized data with different features. The computer then “learns” which features within the dataset separate it into various categories. These features detected by the computer are often subtle and complex and may not be distinguishable by human observers. Once the computer has learned which features correspond to different categories, it can apply that knowledge to determine the category a new dataset belongs to.
How we use machine learning
The Scientific Computing and Imaging (SCI) Institute at the University of Utah is a world leader in biomedical computing and visualization. The SCI Institute has 17 full-time faculty members and around 200 students, programmers, and staff, some of whom also belong to the departments of bioengineering, computing, mathematics, and physics. The overarching research objective of the SCI Institute is to create new scientific computing techniques, tools, and systems that enable solutions to important problems in biomedicine, science, and engineering. The SCI Institute, which was named an Nvidia GPU Center of Excellence, seeks to use the power and versatility of modern computing to drive progress in a variety of fields.
We have used machine learning to detect changes in the cardiac signal that indicate the first signs of a heart attack. Our approach isolates the electrical signals from the heart and examines changes before, during, and after simulated heart attacks. The computer then reads these signals and categorizes the data. The two categories the computer isolates are “having a heart attack” and “not having a heart attack.” Compared to traditional human observers, the computer can determine the onset of a heart attack 10 percent faster. The computer was also 32 percent more accurate at detecting the early signs of a heart attack. Each additional episode detected by the machine learning algorithm is a potentially averted misdiagnosis.
The future of heart attack detection
Using machine learning to help physicians detect heart attacks is advancing the field of cardiology. Physicians and health care workers will soon have a better tool to detect and treat you during one of life’s most dire circumstances. This tool could even protect people who are at risk of heart attacks because of genetic predispositions or environmental factors. This research could contribute to new ways of understanding and detecting heart attacks, perhaps even making death from heart attacks a thing of the past.
If you ever have to visit your doctor for chest pain, check who their partner is — it might just be a computer.
Wilson W. Good is a PhD student researcher with joint appointments at the Scientific Computing and Imaging (SCI) Institute and the Nora Eccles Cardiovascular Research and Training Institute (CVRTI) in the field of cardiac electrophysiology.
Brian Zenger is an MD/PhD student researcher at the Scientific Computing and Imaging Institute, Nora Eccles Cardiovascular Research and Training Institute, and the University of Utah School of Medicine in the field of cardiovascular disease.