Abstract 74: Cardiopulmonary Resuscitation Quality Assessed by Electrocardiography Signal Processing Using Hilbert-Huang Transform Correlates Well with Accelerometer
Bibliographic record
Abstract
Objectives High quality cardiopulmonary resuscitation (CPR) is paramount to patient outcomes in cardiac arrests (CA). Methods to assess CPR quality using widely available data should be explored. This study was aimed to compare CPR quality assessed by surface ECG signal processing vs. accelerometer. Methods Adult patients with CA receiving CPR in the emergency department of a tertiary university hospital, from Dec 2010 to March 2011 were included. CPR was monitored continuously by recording the surface ECG signals and sternal displacement via an accelerometer. CPR quality parameters including total compression numbers, no flow time (pause > 1.5 sec), total flow time (time with pause < 1.5 sec) and average compression rate for up to the first 10 min of CPR were obtained by 1) ECG signal processing algorithm using empirical mode decomposition via Hilbert-Huang Transform (ECG-HHT) and 2) sternal displacement recorded by an accelerometer (ACCEL). Results CPR sessions of 9 CA (6 males, mean age 79.9 years) were analyzed. Compared to ACCEL, ECG-HHT showed lower total compressions, higher no flow time, and lower total flow time. There are good correlations between ECG-HHT vs. ACCEL in total compression numbers (R=0.95) and total flow time (R=0.97). Conclusions ECG-HHT correlates well with ACCEL in certain CPR quality parameters and may be used to assess CPR quality through widely available ECG data.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".