An SCP compatible 12-lead electrocardiogram database for signal transmission, storage, and analysis
Bibliographic record
Abstract
12-lead electrocardiogram (ECG) is one of the most important noninvasive and low-cost diagnostic examinations in clinical practice. However, different manufactory proprietary forms obstacle electronic ECG data transmission and data exchange. The primary objective of this study was to develop an inter-hospital Web-based 12-lead ECG database server for ECG record exchange and analysis in the local area of Miao-Li County in Taiwan. The server was developed using software such as PHP, MySQL, and Matlab for SCP compatible format ECG record file transmission, format conversion, record storage, and signal analysis. In the past year, our efforts resulted in the collection of more than three thousands complete 12-lead SCP-ECG files. The results indicated that (1) SCP format compatible ECG record files can be exchanged and can be converted into ASCII and XML formats through the established server; (2) authorized end users can browse the database and analyze ECG signals by Matlab signal processing related toolboxes. In conclusion, the established ECG database server can provide effective medical informatics services such as online ECG pattern recognition and diagnosis for clinical physicians and ECG signal processing for researchers. In the future, our work may include other medical signals such as holter ECG and exercise ECG. More efforts will also extend to establish a countrywide inter-hospital medical signal database.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.013 |
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".