Quality-guaranteed steganographed-MECG signal compression using adaptive truncation of DCT and SVD coefficients and ASCII encoding
Bibliographic record
Abstract
With the proliferation of wearable healthcare devices and garments in the last decade, the necessity of the storage capacity of the acquired biomedical signals; particularly multi-lead electrocardiogram (MECG) signals, and the importance of securing users’ personal information have increased significantly. However, existing MECG compression and steganography algorithms are insufficient to address these challenges effectively. This paper presents a discrete cosine transform, singular value decomposition, and American standard code for information interchange (ASCII) character encoding-based highly efficient quality-guaranteed steganographed MECG compression algorithm. The algorithm is tested on three publicly available MECG databases totalling 2.98 months, and its performance is assessed through both qualitative and quantitative measures. The algorithm attains a compression ratio that is much higher than that provided by other algorithms that are developed to compress the MECG signals only. The benefits of using the proposed algorithm are fivefolds: first, the clinical qualities of the reconstructed MECG signals can be controlled precisely, second, user’s personal information is restored with no error, third, reconstruction error of the MECG signals is dependent neither on the size of the user’s information nor on the steganography operation, fourth, the probability of guesstimating the security-key is close to zero, and fifth, high compression performance.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 0.000 |
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".