Discrete wavelet transform and singular value decomposition based ECG steganography for secured patient information...

Area of Science:

• Biomedical Engineering
• Digital Signal Processing
• Information Security

Background:

• Secure transmission of sensitive patient data is crucial in healthcare.
• Existing methods for embedding information in ECG signals face challenges in maintaining signal integrity and diagnosability.
• Electrocardiogram (ECG) signals offer potential as a carrier for steganography due to their ubiquity.

Purpose of the Study:

• To develop and evaluate a novel ECG steganography technique for secure transmission of patient information.
• To investigate the effectiveness of combining Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD) for data embedding in ECG signals.
• To assess the impact of the proposed method on ECG signal diagnosability and data security.

Main Methods:

• Decomposition of ECG signals using Discrete Wavelet Transform (DWT).
• Embedding secret information into the two-dimensional (2D) ECG image using Singular Value Decomposition (SVD) by replacing singular values.
• Performance evaluation using metrics such as Kullback-Leibler divergence (KL), Percentage Residual Difference (PRD), Peak Signal to Noise Ratio (PSNR), and Bit Error Rate (BER).
• Dynamic location selection for embedding singular values.

Main Results:

• The HH sub-band was identified as the optimal location for embedding secret data within the decomposed ECG signal.
• The proposed method achieved very low signal degradation (less than 0.6%), even with large amounts of secret data.
• Performance metrics confirmed the reliability and security of the data embedding process.
• The integrity of ECG signal diagnosability was maintained post-embedding.

Conclusions:

• The proposed ECG steganography approach using DWT and SVD is a reliable and secure method for transmitting patient personal information.
• The technique effectively balances data security with minimal impact on the diagnostic quality of ECG signals.
• The HH sub-band is recommended for embedding data to ensure optimal performance and maintain signal interpretability.