基于IoMT的智能医疗检测系统由量子区块链和量子神经网络驱动
IEEE journal of biomedical and health informatics
|July 3, 2023
概括
这项研究介绍了一种使用量子区块链进行安全的心电图存储的量子心律失常检测系统 (QADS),以及用于准确检测心律失常的混合量子神经网络,从而增强心血管疾病诊断.
科学领域:
- 量子计算是一种量子计算.
- 医疗信息学 医疗信息学
- 心血管健康 心血管健康
背景情况:
- 电心电图 (ECG) 泄漏是医疗物联网 (Internet of Medical Things) 的一个日益关注的问题.
- 经典的区块链安全性对于量子时代的ECG数据是不够的.
- 安全有效地检测心律失常对于诊断心血管疾病至关重要.
研究的目的:
- 提出一个量子心律失常检测系统 (QADS),用于安全存储和共享心电图数据.
- 开发一种基于量子的方法来识别异常的心电图数据和诊断心血管疾病.
- 通过使用量子区块链技术提高ECG数据管理的安全性和实用性.
主要方法:
- 实现一个量子区块链算法与控制的量子步行哈希函数和量子身份验证协议.
- 构建一个混合量子卷积神经网络 (HQCNN) 来提取时间心电图特征.
- 在QADS框架内利用量子神经网络进行异常心电图数据识别.
主要成果:
- HQCNN的平均训练准确率为94.7%,测试准确率为93.6%.
- 与经典的CNN相比,HQCNN展示了优越的检测稳定性和对抗量子噪声的稳定性.
- 数学分析证实了量子区块链算法对量子攻击的强大安全性.
结论:
- QADS提供了一种安全而实用的解决方案,用于使用量子区块链进行ECG数据存储和共享.
- 该HQCNN有效检测异常心跳,改善心血管疾病的诊断.
- 拟议的系统提供强大的安全性,防止复杂的量子攻击,确保数据完整性.
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