ECG Steganography ensures protection of patient data when ECG signals embedded with patient data are
transmitted over the internet. Steganography algorithms strive to recover the embedded patient data entirely
and to minimize the deterioration in the cover signal caused by the embedding. This paper presents
a Continuous Ant Colony Optimization (CACO) based ECG Steganography scheme using Discrete Wavelet
Transform and Singular Value Decomposition. Quantization techniques allow embedding the patient data
into the ECG signal. The scaling factor in the quantization techniques governs the tradeoff between imperceptibility
and robustness. The novelty of the proposed approach is to use CACO in ECG Steganography,
to identify Multiple Scaling Factors (MSFs) that will provide a better tradeoff compared to uniform
Single Scaling Factor (SSF). The optimal MSFs significantly improve the performance of ECG steganography
which is measured by metrics such as Peak Signal to Noise Ratio, Percentage Residual Difference,
Kullback–Leibler distance and Bit Error Rate. Performance of the proposed approach is demonstrated on
the MIT-BIH database and the results validate that the tradeoff curve obtained through MSFs is better
than the tradeoff curve obtained for any SSF. The results also advocate appropriate SSFs for target imperceptibility
or robustness