Cross-Interval Continuous Unification Model for Abnormal Heart Rhythm Detection Using ECG Signals
Bibliographic record
Abstract
Electrocardiogram (ECG) signals observed from wearable sensing devices follow a cyclic and continuous pattern for detecting heart-related diseases.Signal processing for medical psychological vital observation identifies the random or irregular rhythms guided by machine learning and artificial intelligence techniques.In this article, a Cross-Interval Continuous Unification Model (CICUM) is introduced to identify such irregular rhythms in continuous observation intervals.This model is backboned by a two-layer neural network for continuity verification and signal correlation.The first layer is used to identify discontinuous sequences between fixed signal sensing intervals.The second layer is responsible for correlating the peak and lower-order signal pulses with the normal and abnormal ECG training inputs.In the first layer, the continuous time interval metric is used to identify discontinuities that are converged using identified signal iterations.In the second layer, the variations between high and low pulses are used to train the neural network for precise abnormal signal detection.Therefore, the proposed model unifies cross intervals and signal correlations between abnormal and normal sequences to detect heart-related diseases from irregular psychological signals.The CICU improves the sequence classification, detection accuracy, and continuity verification by 14.71%, 8.91%, and 11.82% respectively for the maximum sequences.
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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".