# Machine Learning in Atrial Fibrillation

> **NIH NIH R01** · STANFORD UNIVERSITY · 2022 · $749,115

## Abstract

Project Summary
 Atrial fibrillation (AF) is the most common heart rhythm disorder, affecting 2 million Americans in
whom it may cause skipped heart beats, dizziness or stroke. Unfortunately, therapy for AF has limited
success, likely because AF represents heterogenous and poorly characterized disease entities between
individuals. A central challenge is that it is not clear why a specific therapy works in a given AF patient.
This uncertainty makes it challenging to develop a patient-specific approach to tailor therapy for
personalized medicine.
 The premise of this project is that mechanistic data is increasingly available in AF patients at
scales spanning tissue, whole heart and patient levels, yet rarely integrated. We set out to use machine
learning (ML), a powerful approach proven to classify complex datasets, to integrate data to address 3
clinical unmet needs. First, electrograms are rarely used to guide therapy in AF, unlike organized
rhythms, because they are difficult to interpret. Second, it is difficult to understand how arrhythmia is
affected by any specific ablation strategy in AF, unlike organized rhythms. This makes it difficult to
improve therapy. Third, it is difficult to identify whether an individual patient will or will not have success
from AF ablation. We applied machine learning and novel objective analyses to these questions to
develop strategies for personalized AF therapy.
 We have 3 specific aims: (1) To identify components of AF electrograms using ML trained to
monophasic action potentials (MAP); (2) To identify electrical and structural features of the acute
response of AF to ablation near and remote from PVs; (3) To identify patients in whom ablation is
unsuccessful or successful long-term, who are poorly separated at present. Each Aim will compare ML to
traditional biostatistics, and use objective explainability analysis of ML to provide mechanistic insights.
 This study has potential to deliver immediate clinical and translational impact. We will apply
specific ML approaches, biostatistics, and computer modeling to our rich multiscale registry. We will
develop practical and shareable tools, which we will prospectively test clinically, to deliver meaningful
outcomes at tissue, whole heart and patient scales. Our team is experienced in electrophysiology,
computer science, signal processing and biological physics. This project is likely to reveal novel
multiscale AF phenotypes to enable personalized therapy.

## Key facts

- **NIH application ID:** 10347364
- **Project number:** 5R01HL149134-03
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Sanjiv M Narayan
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $749,115
- **Award type:** 5
- **Project period:** 2020-04-01 → 2025-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10347364

## Citation

> US National Institutes of Health, RePORTER application 10347364, Machine Learning in Atrial Fibrillation (5R01HL149134-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10347364. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*