A NOVEL TIME-DOMAIN DIAGNOSTIC METHOD FOR ECG SIGNAL SYSTEM BASED ON A SMART-PHONE
Bibliographic record
Abstract
This thesis presents a novel method on a Smart-phone for ECG tele-monitoring signal\nanalysis. The proposed system focuses on QRS complex detection, beat classi?cation\nand arrhythmias classi?cation. In the regular process, the QRS complex is detected\nby the Pan-Tompkins algorithm and classi?ed as normal sinus rhythms (SRs) or pre-\nmature ventricular contractions (PVCs) by existing classi?cation methods. Subse-\nquently, the Lempel-Ziv (LZ) complexity measure, including the K-Means clustering\nalgorithm and the LZ complexity analysis, is utilized to further separate the high\nrisk arrhythmias, ventricular tachycardia (VT) or ventricular ?brillation (VF). In\nthis procedure of the high risk arrhythmias, three consecutive PVC beats in a row\nare considered to be an indication of the beginning of VT rhythms, at which point\nthe following data points will be saved until up to a certain window length long are\nreached. The window length long ECG signal will be further classi?ed as VT or VF\nby several new decision rules with heart rate detection. Furthermore, the proposed\nsystem successfully implemented on a Smart-phone adopts the time frames to indicate\nthe analysis report for improving the reliability and error detection of arrhythmias.\nThe new analysis method presents fairly good performance results when applied to\ntesting records chosen from the MIT-BIH 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".