The psychophysiological technology with biofeedback in complex rehabilitation of post-stroke patients: a randomized controlled study
Bibliographic record
Abstract
INTRODUCTION. Psychophysiological technologies with biofeedback (BFB) are a promising area of in medical rehabilitation (MR) for post-stroke patients. AIM. To evaluate the effectiveness of the MR-program including psychophysiological technology with BFB for patients with an ischemic stroke (IS). MATERIALS AND METHODS. The study includes patients in the early recovery period of IS with mild to moderate motor deficits, cognitive dysfunction and emotional-volitional disorders. The complex MR program for the main group 1 patients (MG1, n = 30) included psychophysiological BFB-technology based on the beta-rhythm parameters of Electroencephalography (EEG); the main group 2 (MG2) patients (n = 55) received psychophysiological trainings with BFB based on heart rate. The patients of the control groups (KG1, n = 30 and KG2, n = 30) received only standard MR. To control the effectiveness of MR, we analyzed the changes in physiological parameters (HR, blood pressure (BP)), EEG parameters, cardiointervalography (CIG), as well as the results of the Montreal Cognitive Assessment Scale (MoCA), the well-being, activity, and mood questionnaire (WAM), the Hospital Anxiety and Depression Scale (HADS), and the Motivation for Success test according to T. Ehlers. RESULTS AND DISCUSSION. BFB-training based on the EEG beta rhythm showed a statistically significant improvement in the cognitive status of the patients in MG1 according to the MoCA, a decrease in the level of anxiety and reaction to external influences, which was confirmed by the dynamics of systolic BP (SBP) and HR, as well as the achievement of a moderately high level of motivation with reliable differences from baseline values and compared to Control Group 1 (p 0.05). The course of biofeedback training based on the HR parameters showed a reliable change in the CIG parameters, a decrease in SBP and HR in the patients of MG2; statistically significant increase of the WAM questionnaire parameters (p 0.05). CONCLUSION. The integration of psychophysiological technology with biofeedback in comprehensive rehabilitation protocols enhances the efficacy of post-stroke cognitive-motor rehabilitation, particularly in patients with high cardiovascular risks.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".