Active Projects
Objective: To develop machine learning tools to predict outcomes and improve treatments for patients with cardiovascular disease.
Areas of Focus: Electrocardiography, Vectorcardiography, Machine Learning, Data Science
Identifying which Heart Attack Patients need Immediate Treatment
33% of patients with a heart attack have no obvious signs on ECG but require immediate treatment
Our team is working diligently to produce the first machine learning algorithm to be able to classify these patients so those with an occluded artery get treated faster
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Collaborators: Wojciech Zareba MD, PhD (UR) and Linwei Wang PhD (RIT)
Pregnancy as a "Cardiac Stress Test"
Pregnancy induces significant stress on the heart as indexed by increased heart rate and blood volume.
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We are exploring how heart rate dynamics can phenotype women during pregnancy for later cardiovascular disease risk
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Collaborators: Caitlin Dreisbach PhD, RN (UR)
Real-Time Physiological Monitoring among Firefighters
Firefighters are at the highest risk of Sudden Cardiac Death (SCD) among all occupational groups
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Developing unique, multi-parameter physiological monitoring may help alert firefighters of changes which precede SCD
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Collaborators: Mary G Carey PhD, RN (UR) & Wai Cheong Tam PhD (NIST)
