Key result
FPGA-based wearable patch with deep learning achieves 95% accuracy for continuous AF detection over three weeks.
Why the study?
Scaling commercially available devices and supporting AI algorithms for reliable AF detection to the volume of people needing diagnosis remains a major challenge.
A novel FPGA-based wearable patch with embedded deep learning achieves 95% accuracy for AF detection with highly efficient power consumption (3.8 mW).
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 3.8 mW 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.
No takes yet. Share an insight, caveat, or question.
Loroch et al. (2025) studied Atrial fibrillation. FPGA-based patch-style wearable monitor with deep learning-based AF detection vs. Cardiologist-level performance was evaluated on Atrial fibrillation detection accuracy. An FPGA-based patch-style wearable monitor with embedded deep learning achieved 95% accuracy for continuous atrial fibrillation detection over three weeks.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: