# Home-based videoplethysmographic detection of atrial fibrillation

> **NIH NIH R01** · UNIVERSITY OF ROCHESTER · 2020 · $460,176

## Abstract

Abstract
Atrial fibrillation (AF) prevalence is estimated to 1% of the general population in the United
States. A shocking 30% to 60% of patients with AF are unaware of their diagnosis (silent AF).
Therefore, optimal management of AF patients represents one of the most significant challenges
of modern clinical cardiology. In the proposed project, we will evaluate a novel concept of
patient monitoring utilizing a contactless video-based technology. Our preliminary results
revealed that the presence of AF can be detected from the video of an individual's face. We will
evaluate our AF detection technology when embedded into a tablet that automatically record
facial videos when the user works on email, browses the internet or watches videos. We will
conduct a study in which we will enroll symptomatic AF patients going through either
radiofrequency ablation or electrical cardioversion. We will follow these patients during 14 days
after their procedure. Using a patch continuously recording ECG during the follow-up period, we
will evaluate if the proposed video technology can detect the early recurrence of AF in these
patients. The primary goal of the study is to demonstrate that non-constraining contactless video-
based technology represents a robust home-based monitoring technology for AF detection.

## Key facts

- **NIH application ID:** 9966752
- **Project number:** 5R01HL137617-04
- **Recipient organization:** UNIVERSITY OF ROCHESTER
- **Principal Investigator:** JEAN-PHILIPPE Y COUDERC
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $460,176
- **Award type:** 5
- **Project period:** 2017-07-10 → 2021-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9966752

## Citation

> US National Institutes of Health, RePORTER application 9966752, Home-based videoplethysmographic detection of atrial fibrillation (5R01HL137617-04). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9966752. Licensed CC0.

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