The Use of Complexity and Variability Characteristics for the Analysis of Complex Dynamic Systems
Bibliographic record
Abstract
Introduction.The normal dynamics of a healthy organism is chaotic and the observed "chaos" is inherent in the very nature of the dynamic processes taking place in the organism and the degree of chaotic of these processes may vary in case of pathology in one direction or another.The electrical activity of the brain is also characterized by signs of deterministic chaos, and changes in parameters of its nonlinear dynamics testify to the characteristic changes in brain functioning.The problem of diagnostics and identification of the moment preceding an epileptic seizure or other periods of brain functioning in epileptic patients is not only a problem of choosing a classification method but also of determining quantitative estimates of dynamics reflecting the complexity and variability of the Electroencephalography (EEG) signal.The purpose of the paper is to form an effective ensemble of features from the characteristics reflecting the complexity and variability of the EEG signal,to construct the prognostic models for the course of epilepsy and to develop the information technology to support diagnostic decision-making based on them.Methods.The methods of mathematical statistics for the processing of diagnostic information, the methods of mathematical modeling (stepwise logistic regression) -for the construction of prognostic models for estimating the course of epilepsy were used; methodological bases for the creation of information technology for the diagnosis of epilepsy according to the EEG. Results.Changes in indicators such as Hurst Index, fractal dimension, logistic mapping, and algorithmic signal complexity have been investigated.The mathematical models © BILOSHYTSKA O.K., NASTENKO Ie.A., PAVLOV V.A., 2020 include variables that are calculated from the EEG data and are available during patient observation.As a result of the application of step-by-step algorithms, the most informative features are included in the models.The selected features allow for the most accurate identification of individual periods of epilepsy flow from the EEG data.It has been established that the use of a decision support system increases the reliability of determining the periods of an epileptic seizure (conditional norm, before, during and after an attack) by an average of 6.6% for children and 8% for adults.Conclusions.The proposed prognostic models allow to obtain additional information about the periods of epileptic seizures and to predict their onset in time.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".