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

  • Systems-Level Quality Improvement
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Abstract

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 4.2626 × 10− 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.

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Acknowledgments

The author acknowledge the support of the ntou center for teaching and learning, maritime telemedicine teaching and learning probject, and the valuable comments of the reviewers.

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Authors and Affiliations

  1. Department of Electrical Engineering, National Taiwan Ocean University, Taiwan, Republic of China

    Chin-Feng Lin

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Correspondence to Chin-Feng Lin.

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This article is part of the Topical Collection on Systems-Level Quality Improvement.

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Lin, CF. Chaotic Visual Cryptosystem Using Empirical Mode Decomposition Algorithm for Clinical EEG Signals. J Med Syst 40, 52 (2016). https://doi.org/10.1007/s10916-015-0414-0

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  • DOI: https://doi.org/10.1007/s10916-015-0414-0

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