User Adaptive QRS Detection Based on One Target Clustering and Correlation Coefficient
Bibliographic record
Abstract
This paper presents a computationally efficient user adaptive QRS detection algorithm for wearable devices that achieves an average positive prediction rate of 99.39% and sensitivity rate of 99.21% evaluated on 48 tapes of MIT-BIH arrhythmia database. The proposed user-specific QRS template avoids the complicate models and parameters used in existing algorithms while covers most situations for practical applications. The detection is based on the comparison of the correlation coefficient of the user-specific template extracted from individual user with the input ECG signal segment under detection. To reduce the computation, a novel one-target clustering is proposed to reduce the required loops from (K+1 )K/2 to 1 comparing to a K target group clustering; meanwhile, the detection judgement is triggered by the possible peaks to avoid a continuous time correlation calculation and can be taken as one-point FIR filter operation following by the dividing of the standard derivation of the input ECG signal segment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".