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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

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.

We are exploring how heart rate dynamics can phenotype women during pregnancy for later cardiovascular disease risk

Collaborators: Caitlin Dreisbach PhD, RN (UR)

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Real-Time Physiological Monitoring among Firefighters

Firefighters are at the highest risk of Sudden Cardiac Death (SCD) among all occupational groups

Developing unique, multi-parameter physiological monitoring may help alert firefighters of changes which precede SCD

Collaborators: Mary G Carey PhD, RN (UR) & Wai Cheong Tam PhD (NIST)

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