Optimization of Multiple Scaling Factors for ECG Steganography Using Dynamic Thresholding GA
Keywords:
Steganography, ECG, Genetic Algorithm, Privacy, SecurityAbstract
Protecting patient data has become a top priority for healthcare providers in the digital age. ECG steganography is a technique for concealing electrocardiogram (ECG) signals during Internet transmission along with other medical data. This strategy aims to recover all embedded patient data while minimizing degradation of the cover signal caused by embedding. Quantization techniques make it possible to include patient information in the ECG signal, and it has been discovered that multiple scaling factors (MSFs) provide a superior trade-off than uniform single scaling factors. In this paper, we present a novel contribution to the field: a discrete wavelet transforms and singular value decomposition-based dynamic Thresholding GA (DTGA)-based ECG steganography scheme. Using the MITIH database, we demonstrate the efficacy of this method, and our findings corroborate that DTGA significantly improves data security.
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