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Chaotic Visual Cryptosystem Using Empirical Mode Decomposition Algorithm for Clinical EEG Signals

机译:基于经验模态分解算法的临床脑电信号混沌视觉密码系统

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摘要

This paper, proposes a chaotic visual cryptosystem using an empirical mode decomposition (EMD) algorithm for clinical electroencephalography (EEG) signals. The basic design concept is to integrate two-dimensional (2D) chaos-based encryption scramblers, the EMD algorithm, and a 2D block interleaver method to achieve a robust and unpredictable visual encryption mechanism. Energy-intrinsic mode function (IMF) distribution features of the clinical EEG signal are developed for chaotic encryption parameters. The maximum and second maximum energy ratios of the IMFs of a clinical EEG signal to its refereed total energy are used for the starting points of chaotic logistic map types of encrypted chaotic signals in the x and y vectors, respectively. The minimum and second minimum energy ratios of the IMFs of a clinical EEG signal to its refereed total energy are used for the security level parameters of chaotic logistic map types of encrypted chaotic signals in the x and y vectors, respectively. Three EEG database, and seventeen clinical EEG signals were tested, and the average r and mse values are 0.0201 and 4.2626x10(-29), respectively, for the original and chaotically-encrypted through EMD clinical EEG signals. The chaotically-encrypted signal cannot be recovered if there is an error in the input parameters, for example, an initial point error of 0.000001 %. The encryption effects of the proposed chaotic EMD visual encryption mechanism are excellent.
机译:本文提出了一种基于经验模式分解(EMD)算法的临床脑电图(EEG)信号混沌视觉密码系统。基本设计概念是集成基于二维(2D)混沌的加密加扰器,EMD算法和2D块交织器方法,以实现可靠且不可预测的视觉加密机制。临床EEG信号的能量本征模式功能(IMF)分布特征被开发用于混沌加密参数。临床EEG信号的IMF与其参考总能量的最大和第二最大能量比分别用作x和y向量中加密混沌信号的混沌逻辑映射类型的起点。临床EEG信号的IMF与其参考总能量的最小和第二最小能量比分别用于x和y向量中加密混沌信号的混沌逻辑映射类型的安全级别参数。测试了三个EEG数据库和17个临床EEG信号,原始和通过EMD临床EEG信号进行混沌加密的平均r和mse值分别为0.0201和4.2626x10(-29)。如果输入参数存在错误,例如初始点错误为0.000001%,则无法恢复混沌加密的信号。所提出的混沌EMD视觉加密机制的加密效果非常好。

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