Wavelet transform analysis reveals differences between patients with impaired left ventricular systolic function and healthy individuals
Bibliographic record
Abstract
Abstract Despite continuous progress in medical treatment, heart failure (HF) is the leading cause of hospitalizations. Patients with a left ventricular ejection fraction (LVEF) below 50% are characterized by the highest risk of cardiovascular complications. The objective of this study was to examine how LVEF below 50% and aging impact cardiovascular physiology. Sixteen males with physician diagnosed coronary artery disease and LVEF = 42 ± 6% (age 62 ± 6 years, BMI 29.1 ± 3.8kg/m2) and 10 healthy controls (9 male and 1 female, age 28.5 ± 9.1 years, BMI = 24.1 ± 1.2kg/m2) were recruited in our study. Finger photoplethysmography for blood pressure (BP) and electrocardiogram (ECG) were recorded while participants rested in a supine position. Wavelet transformations were used to analyze the amplitudes, phase coherence and phase difference of BP and ECG. The frequency intervals were separated as follows: I (0.6-2Hz), II (0.145–0.6Hz), III (0.052–0.145Hz), and IV (0.021–0.052Hz). HF patients showed a decrease (p < 0.05) in BP wavelet amplitude intervals III and IV in comparison to controls, and interval I for ECG. A decrease in phase coherence (p < 0.01) at interval I is also found in HF patients compared to controls. A decrease in smooth muscle cell activity and smooth muscle autonomic innervation (intervals III and IV) contributions to BP, along with a decrease in cardiac activity as shown by the wavelet amplitude in ECG, suggests altered BP and ECG function in aging HF patients. Furthermore, a decrease in the cardiac interval represents an impairment in the BP and ECG relationship in HF patients. The wavelet transform has the potential to expand our understanding of LVEF and improve diagnostic procedures and patient prognosis.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".