混合深度学习和离散波段转换基于心律失常患者和健康对照者的心电图生物识别
Muhammad Sheharyar Asif1, Muhammad Shahzad Faisal1, Muhammad Najam Dar2
1Department of Computer Science, COMSATS University Islamabad, Attock City 43600, Pakistan.
Sensors (Basel, Switzerland)
|July 11, 2023
概括
这项研究使用波形变换和深度学习 (1D-CRNN) 的新融合增强了心电图 (ECG) 生物识别. 该方法显著提高了安全应用的识别准确性,即使是短的心电图信号间隔.
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:
- 心电图 (ECG) 信号是新兴的生物识别方式,因为它们的内在和生命检测特性.
- 挑战包括大,多样化的数据集和短的心电图信号间隔的低识别性能.
研究的目的:
- 提出一种新的方法,用于特征级融合离散波波变换 (DWT) 和1D卷积循环神经网络 (1D-CRNN),以增强基于心电图的生物识别.
- 为了应对在具有较短心电图信号间隔的大型群体数据集中识别性能较低的挑战.
主要方法:
- 电脑心电图信号预处理:去除电源线干扰,低通过 (1.5 Hz 切断) 和移除基线漂移.
- 特性提取:传统的使用Coiflets的5DWT在PQRST细分信号和基于深度学习的使用1D-CRNN (2LSTM,3卷积层).
- 传统和深度学习特征的功能级融合.
主要成果:
- 实现的生物识别准确率为80.64% (ECG-ID),98.81% (MIT-BIH) 和99.62% (NSR-DB). 通过使用生物识别技术,可以识别生物识别数据.
- 在将所有数据集结合时,获得了98.24%的准确性.
- 拟议的融合方法单独优于传统和基于深度学习的特征提取,以及在短ECG段上转移学习方法 (VGG-19,ResNet-152,Inception-v3).
结论:
- 拟议的特征级融合方法显著提高了ECG生物识别的准确性.
- 这种方法即使在短的心电图信号段和各种数据集上也是有效的,为法医,监视和安全应用提供了强大的解决方案.
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