THE RELATIONSHIP BETWEEN THE PARAMETERS OF THE HEART RHYTHM AT REST, CALCULATED FROM ECG AND FPG VARIOUS TYPES OF ANALYSIS (TEMPORAL, FREQUENCY AND NONLINEAR)
Bibliographic record
Abstract
Вариабельность ритма сердца может быть оценена как в результате анализа ЭКГ, так и фотоплетизмограммы (ФПГ). В нескольких публикациях (материалах конференций) на небольших выборках была продемонстрирована согласованность по ряду показателей ритма сердца. В настоящем исследовании на расширенной выборке и с использованием трех видов анализа ритма сердца было показано, что все коэффициенты корреляции были статистически значимые, но самая низкая согласованность оказалась для показателей мощностей LF и HF компонентов, а также выборочная энтропия (SampEn). Также было показано, что показатели ВРС не связаны ни с амплитудой пульсовой волны, ни с временем ее распространения. Таким образом, ФПГ может быть использована для расчета показателей ВРС различными методами анализа, а также амплитуды пульсовой волны и (при одновременной регистрации с ЭКГ) и времени распространения пульсовой волны в психофизиологических исследованиях. Heart rate variability can be assessed both as a result of ECG and photoplethysmogram analysis (PPG). In several publications (conference proceedings), consistency in a number of heart rate indicators was demonstrated in small samples. In the present study, using an expanded sample and using three types of heart rate analysis, it was shown that all correlation coefficients were statistically significant, but the lowest consistency was found for the power indicators of LF and HF components and sample entropy. It was also shown that HRV indicators are not related to either the amplitude of the pulse wave or the pulse transit time. Thus, PPG can be used to calculate HRV indicators by various methods of analysis, as well as the amplitude of the pulse wave and (with simultaneous registration with an ECG) and the pulse transition time in psychophysiological studies.
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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 | 0.001 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".