通过智能手表的心电图来估计年龄
Azfar Adib1, Wei-Ping Zhu1, M Omair Ahmad1
1Department of Electrical and Computer Engineering, Concordia University, Montreal, QC Canada.
NPJ biomedical innovations
|October 27, 2025
概括
智能手表的心电图 (ECG) 信号可以准确估计年龄,超过临床心电图方法. 这项技术为年龄验证提供了一个保护隐私的解决方案,特别是用于在线儿童保护.
科学领域:
- 生物医学工程 生物医学工程
- 数字健康数字健康
- 机器学习 机器学习
背景情况:
- 准确的年龄估计对于年龄限制服务和在线儿童安全至关重要.
- 传统的年龄验证方法 (身份验证,面部识别) 存在隐私和可靠性问题.
- 电心电图 (ECG) 信号表现出年龄相关的特征,提供了一个潜在的生物识别替代方案.
研究的目的:
- 为了研究使用智能手表衍生的心电图信号来估计年龄的可行性.
- 开发和评估机器学习模型,使用可穿戴式心电图数据进行年龄预测.
- 为了比较智能手表基于心电图的年龄估计与传统和临床心电图方法的性能.
主要方法:
- 收集了来自 220 名跨越广泛年龄段的智能手表心电图的新型数据集.
- 提取并分析了与年龄相关的生理变化相关的各种心电图特征.
- 训练并测试多个机器学习模型来预测年龄并执行二进制年龄分类.
主要成果:
- 在年龄估计中达到2.93年的平均绝对误差 (MAE),超过了基于心电图的临床研究.
- 在青春期表现出高峰精度,与显著的ECG发育变化相关.
- 在13-21岁的年龄范围内实现了对二元年龄分类的高准确性 (93-96%).
结论:
- 智能手表ECG信号为非侵入性年龄估计提供了可行且准确的方法.
- 这种方法为传统的年龄验证技术提供了一个隐私意识的替代方案.
- 可穿戴式心电图技术在需要可靠的年龄评估的应用中具有重大潜力,特别是在数字环境中.
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