ECG signal protection using redundant discrete wavelet transform-based data hiding
Bibliographic record
Abstract
Telemedicine provides a variety of products and services aimed at expediting the flow of digitized patient records. For efficient consultations, it is essential to centralize patient data, enabling easy access to the patient's medical history during consultations. Consequently, it is crucial for patients to utilize a secure tool with comprehensive security solutions to safeguard their information. In our effort to enhance the security of electrocardiogram (ECG) signals exchanged in telemedicine, we propose a frequency-domain watermarking approach in this chapter. This method involves concealing electronic patient records within corresponding ECG signals. The signal is initially transformed into a two-dimensional (2D) image, and the frequency content is extracted using a redundant discrete wavelet transform (RDWT). The resulting coefficients undergo Schur decomposition, and the watermark bits are incorporated by adjusting the least significant bit of the eigenvalues. Imperceptibility tests demonstrate that this approach generates a watermarked signal closely resembling the original, thereby preserving the diagnostic content. Robustness tests further indicate that the watermark can withstand commonly employed attacks in watermarking.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".