利用一个层次的心电图分类模型来增强生物识别识别
1Department of Computer Engineering, Gachon University, 1342, Seongnam-daero, Sujeong-gu, Seongnam-si, 13120, Gyeonggi-do, Republic of Korea.
Computers in biology and medicine
|November 10, 2024
概括
这项研究引入了一种新的2阶段人工智能系统,用于电心图 (ECG) 用户识别. 该系统即使在压力下也能准确识别用户,增强安全性和现实世界的适用性.
科学领域:
- 生物识别信息 生物识别信息
- 人工智能的人工智能
- 信号处理 信号处理
背景情况:
- 电心电图 (ECG) 信号提供了固有的安全性,因为它具有抗伪造的特性.
- 在物理和认知压力下,心电图信号的变化挑战了一致的用户识别.
- 现有的方法与动态的心电图信号特征作斗争.
研究的目的:
- 利用心电图信号开发一个强大的两阶段用户识别系统.
- 为了提高基于心电图的识别在不同用户压力状态下的准确性.
- 为了提高ECG生物识别的现实生活可用性.
主要方法:
- 一个两阶段系统,集成ECG信号和用户状态信息.
- 在第一阶段对心电图信号状态的分类.
- 在第二个模型中利用特征值来增强动态特征学习.
- 在CSU-BIODB和MIT-BIH ST变更数据库上的绩效评估.
主要成果:
- 实现了92.08% (CSU-BIODB) 和95.83% (MIT-BIH) 的识别准确率.
- 获得了0.9207 (CSU-BIODB) 和0.9369 (MIT-BIH) 的f1分数.
- 与现有的单阶段模型相比,显示了显著的精度改进 (9.3%和36.76%).
结论:
- 拟议的两阶段模型有效地解决了在压力下ECG信号的变化.
- 该系统显著提高了基于ECG的用户识别的准确性和实用性.
- 这项研究为生物识别ECG信号处理的深度学习提供了坚实的基础.
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