Project Description:
Advancing age is cited as the primary reason for atrial fibrillation (A-Fib), as life expectancy continues to gain, atrial fibrillation is on the rise. The lifetime risk of developing A-Fib beyond the age of 55 is 25%. Atrial fibrillation can remain asymptomatic putting patients at health risk for not knowing they have the disease. Stroke is the first manifestation of the arrhythmia and is considered the main debilitating health risk associated with A-Fib. Early detection of the arrhythmic disease is essential to reducing risk to patients.
The goal of the project will be to research new innovative technologies such as gyrocardiogram or mobile EKG to take real time data of the heart’s QRS complex to detect atrial fibrillation.
An ideal screening test should be inexpensive, noninvasive, mobile, and self-administered. Research on A-Fib detection methods have gone beyond the traditional large and clunky EKG strategy to detect. Data can be tested against a patient database located at https://archive.ics.uci.edu/ml/machine-learning-databases/arrhythmia/arr.... The device should be able to detect characteristic A-Fib QRS waveforms, portable, and user friendly. Continuous atrial fibrillation detection technologies will help increase the quality of life for patients, and potentially be leveraged to detect other heart anomalies.
Project Mentor:
Engineering Mentor: Jason Brown, Edwards Life Sciences, Jason_brown@edwards.com
Physician Mentor: Steve Small, MD, PhD, Department of Neurology, UCI, small@uci.edu
Other Resources:
Atrial Fibrillation and other Arrhythmia Database: https://archive.ics.uci.edu/ml/machine-learning-databases/arrhythmia/arr...
Team Members:
Carol Chen, Christina Hanna, Bree Keahi Hannah, Sien Tam, Sabrina Megan Wong