Official websites use .gov

A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS

A lock ( 🔒 ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Need Help

A deep learning artifact removal method for CPR continuity throughout the shock decision in AEDs

Award Number: R01HL173935
ORGANIZATION: NATIONAL HEART, LUNG, & BLOOD INSTITUTE
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 06/01/2025
PERIOD OF PERFORMANCE END DATE: 03/31/2029

Group Awards By:

View Award Description

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 accurate rhythm analysis and pulse checks require pauses in chest compressions. Unfortunately, frequent or prolonged interruptions in chest compressions lead to adverse outcomes after cardiac arrest. Despite previous attempts to work with CPR-contaminated ECG in AEDs, resuscitation performance has been hampered by two major factors: 1) there is no AHA-approved validated shock advisory algorithm during CPR performance, and 2) there is insufficient cardiac arrest data with CPR, which has hampered advancements using deep learning approaches to remove CPR artifacts. To overcome these limitations, we propose to develop a database of CPR artifact data already collected from commercial (Defibtech) defibrillators, and inpatient data from the University of Michigan and Massachusetts General Hospital. Using this database, we will evaluate and optimize the performance of our promising CPR artifact removal algorithm based on deep learning. Once the CPR artifact removal and shockable rhythm detection models are validated, we will integrate our algorithms into the existing ECG monitoring system at the University of Michigan medical center and evaluate their performance in real-world scenarios in real time using new CPR data. Successful completion of our aims will allow continuous evaluation of cardiac rhythms during AED implementation, reduce interruptions in CPR, and improve clinical outcomes. Additionally, a large CPR ECG database will catalyze research focused on improving defibrillator performance, identifying key factors for a successful resuscitation, and reducing morbidity and mortality from cardiac arrest.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2026 ( Subtotal = $696,353 )
2026	2026	UNIVERSITY OF CONNECTICUT	438 WHITNEY RD EXTENSION UNIT 1133	STORRS	CT	06269	CAPITOL	USA	Cardiovascular Diseases Research	000	2	6/3/2026	NON-COMPETING CONTINUATION	$696,353
														Subtotal = $696,353

Issue Date FY: 2025 ( Subtotal = $752,495 )
2025	2025	UNIVERSITY OF CONNECTICUT	438 WHITNEY RD EXTENSION UNIT 1133	STORRS	CT	06269	CAPITOL	USA	Cardiovascular Diseases Research	000	1	6/1/2025	NEW	$752,495
														Subtotal = $752,495

Grand Total All Awards = $1,448,848

Top

All Categories

About

Search

Reports

Data Submission

Award Information

A deep learning artifact removal method for CPR continuity throughout the shock decision in AEDs

Award Number: R01HL173935

ORGANIZATION: NATIONAL HEART, LUNG, & BLOOD INSTITUTE

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

PERIOD OF PERFORMANCE START DATE: 06/01/2025

PERIOD OF PERFORMANCE END DATE: 03/31/2029

Federal Websites

Department of Health & Human Services

HHS Operating Divisions

HHS Staff Divisions

Download A Document Viewer