Spatiotemporal Frequency Analysis of Ventricular Fibrillation in Explanted Human Hearts
Bibliographic record
Abstract
Ventricular fibrillation (VF) is a medical condition that occurs due to rapid and irregular electrical activity of heart. If undiagnosed or untreated, VF leads to sudden cardiac death. VF has been studied by researchers for over 100 years to elucidate the mechanism that maintains VF, and thus to arrive at therapeutic options. VF is a nonstationary process, and it manifests into variations in the waveform morphology, phase, and frequency dynamics of the surface electrograms. Dominant frequency analysis (DF maps) and phase maps are two widely used complementary approaches in assessing the evolution of VF process. These techniques are applied to electrograms or fluorescence signals obtained with voltage-sensitive dyes. In spite of VF being a nonstationary process, most of the existing literature limits frequency analysis to a segmented, time-averaged spectral analysis, where valuable information on the instantaneous temporal evolution of the spectral characteristics is lost. In order to resolve this issue, in this paper, we present a joint time-frequency approach that is suited for VF analysis and demonstrate the application of instantaneous mean frequency (IMF) in interpreting VF episodes. Human VF sources are rarely anatomically stable and are migratory. Traditional DF techniques fail in tracking this migratory behavior. IMF, on the other hand, can deal with these migratory sources and conduction blocks better than DF approaches. Results of the analysis using the electrograms of 204 VF segments obtained from 13 isolated human hearts (explanted during cardiac transplantation) indicate that in 81% of the VF segments, there were significant changes in the spatiotemporal evolution of the frequency, suggesting that IMF provides better mechanistic insight of these signals. The IMF tool presented in this paper demonstrates potential for applications in tracking frequency patterns, conduction blocks, and arriving at newer therapies to modulate VF.
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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 | 0.001 | 0.001 |
| 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".