使用12导电心电图信号和图形卷积网络增强生物识别
Maram Al Alfi1, Pedro Peris-Lopez1, Carmen Camara1
1Computer Science and Engineering Department, University Carlos III of Madrid, Madrid, Spain.
Frontiers in digital health
|April 23, 2025
概括
本研究引入了一种使用心电图 (ECG) 信号和图形卷积网络 (GCN) 的新实时生物识别认证系统. 这种新的方法实现了100%的准确性,用于安全的用户识别.
科学领域:
- 生物识别信息 生物识别信息
- 信号处理 信号处理
- 机器学习 机器学习
背景情况:
- 电心电图 (ECG) 信号提供了一种安全的生物识别方式,因为它具有固有的生理特征,可以抵抗伪造.
- 传统的生物识别系统面临着伪造和外部攻击的挑战,需要更强大的身份验证方法.
研究的目的:
- 开发一种新的实时生物识别认证系统,利用图形卷积网络 (GCN) 和来自心电图信号的相互信息 (MI) 指数.
- 通过先进的信号处理和机器学习技术,提高生物识别的安全性和效率.
主要方法:
- 从12导线心电图信号中提取了相互信息 (MI) 指数,以量化导线之间的统计依赖.
- 使用ECG特征作为节点和MI值作为边缘权重构建了一个图表表示.
- 在构建的图形上训练了一个图形卷积网络 (GCN) 模型,以便有效识别用户.
主要成果:
- 拟议的GCN-MI模型以5层架构和75的k-fold实现了100%的准确性.
- 与传统方法相比,该系统表现出更高的性能,需要更少的培训数据.
- 该方法被证明是可扩展的,适合实时应用.
结论:
- 整合MI指数和GCN为基于心电图的生物识别提供了一个强大的和高效的特征选择机制.
- 基于图形的学习框架有效地捕捉空间和统计ECG数据关系,提高分类准确性.
- 这种新的GCN-MI方法为各种应用中实时,安全的生物识别身份验证设定了新的基准.
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