Low-power, Energy-efficient, Cardiologist-level Atrial Fibrillation Detection for Wearable Devices

TL;DR

This paper introduces a low-power, wearable device with embedded deep learning that reliably detects atrial fibrillation at cardiologist-level accuracy, enabling continuous monitoring for weeks with minimal energy consumption.

Contribution

The paper presents a novel FPGA-based wearable monitor with embedded deep learning, achieving ultra-low power consumption and high accuracy for AF detection, surpassing existing solutions.

Findings

Operates at 3.8mW power, 1-3 orders lower than state-of-the-art

Achieves 95% accuracy in AF detection

Enables over three weeks of continuous monitoring

Abstract

Atrial fibrillation (AF) is a common arrhythmia and major risk factor for cardiovascular complications. While commercially available devices and supporting Artificial Intelligence (AI) algorithms exist for reliable detection of AF, the scaling of this technology to the amount of people who need this diagnosis is still a major challenge. This paper presents a novel wearable device, designed specifically for the early and reliable detection of AF. We present an FPGA-based patch-style wearable monitor with embedded deep learning-based AF detection. Operating with 3.8mW system power, which is 1-3 orders of magnitude lower than the state-of-the-art, the device enables continuous AF detection for over three weeks while achieving 95% accuracy, surpassing cardiologist-level performance. A key innovation is the combination of energy-efficient hardware-software co-design and optimized power management through the application of hardware-aware neural architecture search. This advancement represents a significant step toward scalable, reliable, and sustainable AF monitoring.