Life-Threatening Ventricular Arrhythmia Detection With Personalized Features
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The timely detection of life-threatening ventricular arrhythmias (VAs) is critical for saving a patient's life. General features that characterize ECG waveforms are extracted for VA detection. To take into account the subtle differences in the QRS complexes among different people, new personalized features are proposed in this paper based on the correlation coefficient between a patient-specific regular QRS-complex template and his/her real-time ECG data. Small sets of the most effective features are chosen with support vector machines from 11 newly extracted and 15 previously existing features, for efficient performance and real-time operation. Our proposed new features aveCC and medianCC are verified to be effective in enhancing the performance of existing features under both the record-based and database-based data divisions. Through 50-time random record-based data divisions, all combinations of two features and three features are tested. The top two-feature combination is VFleak and aveCC, which achieves an area under curve (AUC) value of 98.56%±0.89%, a specificity (SP) of 94.80%±2.15%, and an accuracy (ACC) of 94.66% ± 1.97%; the top three-feature combination is VFleak, MEA, and aveCC, which obtains an AUC of 98.98% ± 0.58%, an SP of 95.56% ± 1.45%, and an ACC of 95.46% ± 1.36%; these results outperform the previous top-two and top-three feature combinations. Similar results are obtained on the database-based data division.
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.
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 it