NIH R01 · 2025
A deep learning artifact removal method for CPR continuity throughout the shock decision in AEDs
PROJECT SUMMARY Out-of-hospital cardiac arrest (OHCA) affects more than 475,000 people in the United States each year. Major determinants of survival from OHCA include continuous high quality cardiopulmonary resuscitation (CPR) coupled with timely defibrillatory shock, if warranted, based on accurate shock decisions. According to the American Heart Association (AHA), delivering continuous CPR along with rapid shock by automated external defibrillator (AED) are two main interventions that are likely to restart the arrested heart. However, the mechanical activity of chest compressions during CPR performance induces severe motion artifact in the electrocardiogram (ECG) signal. Thus, an…
From the public funding record at NIH RePORTER. Describes the funded project, not the reviews below.