# Novel Algorithm and Data Strategies to detect and Predict atrial fibrillation for post-stroke patients (NADSP)

> **NIH NIH R01** · EMORY UNIVERSITY · 2023 · $700,615

## Abstract

Project Summary
Atrial fibrillation (AF) is the most common arrhythmia, affecting 33.5 million people globally with a
growing prevalence. AF is associated with significant morbidity and mortality, including 20% of all
strokes, 33% of hospitalizations related to cardiac arrhythmias, and a two-fold increase in risk of
death. To reduce AF-associated risks such as stroke, it is important to be able to diagnose AF early in
the AF trajectory when it is asymptomatic and paroxysmal in order to initiate effective stroke
prevention interventions including anticoagulation. Unfortunately, it is estimated that 700,000 people
in the USA may have previously unknown AF, and newly detected AF at the time of stroke was found
among 18% of AF-associated stroke incidents. Plethysmography (PPG) measures pulsatile blood
volume changes and is available in up to 71% of consumer wearables. Because of this unmatched
availability, PPG-based AF detection is ideally poised to enable low-cost, long-term, and continuous
AF monitoring at scale. However, modest performance of PPG-based AF detection when PPG
signals do not have perfect signal quality remains a critical impediment to fully realize its potential as
an AF-monitoring tool at scale. The proposed study aims to overcome this challenge by pursuing the
following aims: 1) design, develop, and validate a novel deep neural network (DNN) architecture that
integrate PPG signal quality assessment with AF detection to accurately detect AF even for signals
with imperfect signal quality; 2) validate and test further personalization of the proposed DNN using
prospective data from post stroke patients to be collected in ambulatory settings; 3) develop and
validate interpretable EHR-data driven machine learning approaches to identify patients with elevated
risk of AF for whom PPG-based AF monitoring can be most likely beneficial.

## Key facts

- **NIH application ID:** 10561108
- **Project number:** 1R01HL166233-01
- **Recipient organization:** EMORY UNIVERSITY
- **Principal Investigator:** Xiao Hu
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $700,615
- **Award type:** 1
- **Project period:** 2023-03-10 → 2027-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10561108

## Citation

> US National Institutes of Health, RePORTER application 10561108, Novel Algorithm and Data Strategies to detect and Predict atrial fibrillation for post-stroke patients (NADSP) (1R01HL166233-01). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10561108. Licensed CC0.

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