Hasan Saeed
Pronouns:
Research Mentor(s): Hakan Oral
Co-Presenter:
Research Mentor School/College/Department: Internal Medicine / Medicine
Presentation Date: April 20
Presentation Type: Poster
Session: Session 4 – 2:40pm – 3:30 pm
Room: League Ballroom
Authors: Hasan Saeed, Hakan Oral
Presenter: 75
Abstract
Atrial flutter is a type of cardiac arrhythmia characterized by the rapid pumping of the heart’s upper chambers–the atria. While many arrhythmias are accompanied by irregular heartbeats, cases of atrial flutter often display a regular rhythm on an electrocardiogram (ECG). This regular rhythm may appear almost identical to a healthy rhythm, which makes the detection of atrial flutter a challenge for current detection algorithms. During an atrial flutter event, the atria do not completely contract (known as fluttering), due to an error in the electrical impulses of the heart. Failure to completely contract causes blood to pool in the upper chambers of the heart, which can lead to the formation of a blood clot, and ultimately cause a stroke. The ability to accurately detect an atrial flutter event may be a matter of life and death, and current bedside heart monitors are ineffective in atrial flutter detection. The purpose of this research is to develop an atrial flutter detection algorithm using machine learning models.
Biomedical Sciences, Engineering, Interdisciplinary, Natural/Life Sciences
