Automated classification of congestive heart failure severity using time domain, frequency domain and non-linear heart rate variability measures
Bibliographic record
Abstract
Congestive Heart Failure (CHF) is one of the leading causes of death in elderly in Canada. It has a 5-year survival rate of around 50% and half a million Canadians live this disease. CHF is a progressive disease that rapidly increases in severity. As a result, CHF patients have to pay unscheduled visits to the hospital due to critical emergencies. Lack of automated techniques for the prediction and detection of CHF not only degrades the quality of life of these patients but also causes them extreme financial stress. Automated techniques for the detection of critical events can help clinicians monitor these patients' cardiac health more efficiently. In this paper, we present an automated classifier for the detection of CHF severity. We classified New York Heart Association class I, II and III patients using time domain, frequency domain and non-linear heart rate variability (HRV) measures. We compared the performance of our multi-class classifier and the binary classifier using different sets of HRV features. Our results show that using a combined set of features instead of Standard Deviation of NN intervals (SDNN) alone, improves the classifier accuracy by almost 21%. Moreover, using HRV measures extracted from longer duration of NN intervals, improve the classification accuracy of class I in multi-class classifier by almost 3 times.
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.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".