可穿戴设备衍生出心电图年龄及其与心房动相关性
Seung Hyun Park1, Ju Hyun Jin1, Jongwoo Kim2
1Yonsei University College of Medicine, Seoul, Republic of Korea.
NPJ digital medicine
|January 17, 2026
概括
人工智能 (AI) 可以从单可穿戴设备估计心电图 (ECG) 年龄. 较大的心电图年龄差距与心房 (AF) 风险和负担的增加有关.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 数字健康数字健康
背景情况:
- 人工智能 (AI) 衍生的心电图 (ECG) 年龄显示为心房动 (AF) 风险的标志物具有前途.
- 从单线,可穿戴设备中估计心电图年龄对于广泛应用至关重要.
研究的目的:
- 开发和验证一个人工智能模型 (PROPHECG-Age Single) 来估计可穿戴单线心电图的心电图年龄.
- 在现实世界自我监测环境中调查ECG-年龄差距与AF存在和负担之间的联系.
主要方法:
- 一个基于网络的剩余AI模型被训练在100万个12ECG上,这些ECG被转换为合成单信号,使用循环一致的生成对抗网络.
- 该模型在两个独立的可穿戴队列 (S-Patch和Memo Patch) 中得到验证.
- 分析了ECG年龄差距与AF存在和负担的关联.
主要成果:
- 人工智能模型在两个验证队伍中实现了平均绝对误差10.01年和11.88年.
- 在心电图年龄差距和AF存在之间发现了显著的关联 (几率比为1.03每1年的差距).
- 每增加1年的ECG-年龄差距对应于AF负担增加0.8个百分点.
结论:
- 基于可穿戴的AI-ECG年龄是一个可行的,潜在的有价值的数字生物标志物.
- 这项技术可能能够对像AF这样的疾病进行主动的心血管监测.
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