Automated classification of congestive heart failure severity using time domain, frequency domain and non-linear heart rate variability measures
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
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.
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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 | 0.004 | 0.001 |
| 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