在智能医疗保健系统中使用ECG信号进行人类身份验证的整体语网络 (ESN)
Mehdi Hazratifard1, Vibhav Agrawal1, Fayez Gebali1
1Department of Electrical and Computer Engineering, University of Victoria, Victoria, BC V8W 2Y2, Canada.
Sensors (Basel, Switzerland)
|July 11, 2023
概括
本研究介绍了一个集体语网络 (ESN),用于在数字医疗保健中使用心电图 (ECG) 信号进行持续的用户身份验证. 该ESN模型为远程医疗和智能医疗保健应用提供了强大的和可访问的安全解决方案.
科学领域:
- 生物医学工程 生物医学工程
- 计算机科学 计算机科学
- 网络安全 网络安全
背景情况:
- 数字医疗保健系统使远程患者监测和访问成为可能.
- 持续的身份验证提高了对敏感健康数据访问的安全性.
- 现有的机器学习认证模型面临着用户注册和不平衡数据集的挑战.
研究的目的:
- 为数字医疗保健系统提出使用心电图 (ECG) 信号的新型身份验证方法.
- 开发一个集体语网络 (ESN),能够处理ECG信号的微妙变化,以实现可靠的身份验证.
- 解决当前机器学习认证模型的局限性,包括用户注册和数据集不平衡.
主要方法:
- 使用了易于在数字医疗保健中获得的ECG信号,用于用户身份验证.
- 开发并实施了一个集体语网络 (ESN) 架构.
- 包含用于特征提取的预处理,以提高模型性能.
- 在ECG-ID和PTB基准数据集上训练和评估ESN模型.
主要成果:
- 在ECG-ID数据集上达到93.6%的高准确率,在PTB数据集上达到96.8%.
- 在各自的数据集上获得了1.76%和1.69%的低等错误率.
- 证明了该模型在处理ECG信号中微小变化的有效性.
结论:
- 拟议的ESN模型提供了一个强大的,简单的,可用数据的解决方案,用于数字医疗保健中的持续身份验证.
- 使用ESN的基于ECG的身份验证是提高智能医疗保健和远程医疗安全性的有希望的方法.
- 该方法克服了与用户注册和基于机器学习的身份验证数据集不平衡相关的挑战.
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