基于数据生成网络和非信托数据处理的电心图识别
Ziyang Gong1, Zhenyu Tang2, Zijian Qin2
1Department of Computer Engineering, Gachon University, Seongnam-si, 13120, Republic of Korea.
Computers in biology and medicine
|March 24, 2024
概括
本研究引入了一种使用心电图 (ECG) 信号进行快速准确的生物识别识别的新方法. 这种方法显著降低了错误识别率,提高了基于ECG的身份验证的可靠性.
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:
- 生物信号越来越多地用于身份识别.
- 准确和快速的生物识别方法对组织至关重要.
- 心电图 (ECG) 信号提供了一个有前途的生物识别模式.
研究的目的:
- 开发一种新的算法,用于预处理心电图 (ECG) 数据以进行身份识别.
- 提高基于心电图的生物识别系统的准确性和速度.
- 通过数据增强来提高分类网络的性能.
主要方法:
- 一个线性心电图数据预处理算法,使用卡尔曼波器来降低噪音.
- 实施数据生成战略网络 (DRCN) 以增强培训数据.
- 将DRCN与卷积分类网络集成.
主要成果:
- 在基于ECG的身份识别中,平均错误识别率为2.5%.
- 每个类别的平均认可率为98.7%.
- 与之前的ECG身份识别方法相比,显著改进.
结论:
- 拟议的方法为基于ECG的身份识别提供了一个快速而准确的解决方案.
- DRCN有效地增加了数据,提高了分类性能.
- 这种方法有可能在生物识别安全中得到广泛应用.
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