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Updated: Sep 12, 2026
Simultaneous Scalp Electroencephalography (EEG), Electromyography (EMG), and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
Published on: July 26, 2013
Multi-scroll chaotic attractors in a memristive-cyclic Hopfield neural network for ECG signal encryption in
Haneche Nabil1, Hamaizia Tayeb2
1Applied Mathematics and Modeling Laboratory, Department of Mathematics, Constantine 1 University, Constantine, 25000, Algeria.
Abstract:
The expansion of telemedicine requires robust protection of physiological data. To meet this need, this paper introduces an electrocardiogram (ECG) encryption framework based on a six-dimensional memristive-cyclic Hopfield neural network (MC-HNN) combined with an adaptive non-uniform partition scheme. The MC-HNN produces chaotic sequences with controllable multi-scroll attractors, while an adaptive cubic-spline partition method captures the nonstationary characteristics of ECG signals to derive signal-dependent initial conditions. These sequences drive a permutation and double-diffusion encryption process. Experiments on the MIT-BIH, PTB-XL, CinC Challenge 2017, and CPSC2018 datasets yield a Number of Sample Change Rate (NSCR) of 100%, an average Unified Average Change Intensity (UACI) of 33.42%, near-zero correlation, entropy approaching 8 bits, and uniform histograms. The scheme resists differential, statistical, and chosen-ciphertext attacks. Encryption of 10-second ECG segments requires less than 0.05 s, demonstrating computational efficiency suitable for practical telemedicine deployment.
