Low-power, Energy-efficient, Cardiologist-level Atrial Fibrillation Detection for Wearable Devices
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916905927835648 |
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| author | Loroch, Dominik Feldmann, Johannes Rybalkin, Vladimir Wehn, Norbert |
| author_facet | Loroch, Dominik Feldmann, Johannes Rybalkin, Vladimir Wehn, Norbert |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2508_13181 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Low-power, Energy-efficient, Cardiologist-level Atrial Fibrillation Detection for Wearable Devices Loroch, Dominik Feldmann, Johannes Rybalkin, Vladimir Wehn, Norbert Hardware Architecture Signal Processing 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. |
| title | Low-power, Energy-efficient, Cardiologist-level Atrial Fibrillation Detection for Wearable Devices |
| topic | Hardware Architecture Signal Processing |
| url | https://arxiv.org/abs/2508.13181 |