Email this article Neural network analysis of heart rhythm variability for diagnosis of immobilization syndrome and objectivization of effectiveness of early rehabilitation
- Authors: Nekrasova J.Y.1,2, Yankevich D.S.1, Kanarsky М.М.1, Markov A.S.2
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Affiliations:
- Federal Research and Clinical Center of Intensive Care and Rehabilitology
- Moscow Aviation Institute (National Research University)
- Issue: Vol 23, No 1 (2020)
- Pages: 9-17
- Section: Articles
- URL: https://rjmseer.com/1560-9537/article/view/34233
- DOI: https://doi.org/10.17816/MSER34233
- ID: 34233
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Abstract
The article discusses the use of a neural network analysis of heart rate variability for the diagnosis of immobilization syndrome and post-intensive care syndrome (PICS) in patients with disorders of consciousness for monitoring the quality of the rehabilitation process. It is shown that there are statistical differences between the curves characterizing the heart rate variability of healthy patients and patients with impaired consciousness. The use of a neural network allows to automatically evaluate the severity of the immobilization syndrome and Post Intensive Care Syndrome, as well as the effectiveness of measures for their prevention and the overall quality of the work of medical personnel.
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About the authors
Julia Yu. Nekrasova
Federal Research and Clinical Center of Intensive Care and Rehabilitology; Moscow Aviation Institute (National Research University)
Author for correspondence.
Email: nekrasova84@yandex.ru
ORCID iD: 0000-0002-4435-8501
SPIN-code: 8947-4230
Russian Federation, Moscow
D. S. Yankevich
Federal Research and Clinical Center of Intensive Care and Rehabilitology
Email: yanson_d@mail.ru
ORCID iD: 0000-0001-5143-7366
SPIN-code: 6506-8058
к.м.н.
Russian Federation, MoscowМ. М. Kanarsky
Federal Research and Clinical Center of Intensive Care and Rehabilitology
Email: kanarMM@yandex.ru
ORCID iD: 0000-0002-7635-1048
SPIN-code: 1776-1160
Russian Federation, Moscow
A. S. Markov
Moscow Aviation Institute (National Research University)
Email: arxitektor33@yandex.ru
студент
Russian Federation, MoscowReferences
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Supplementary files
Supplementary Files
Action
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JATS XML
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Figure: 1. Increments of the amplitudes of R-peaks, intervals between R-peaks and angle α
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Figure: 2. Statistical characteristics of the main group of patients
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Figure: 3. Dependences of dR (marked in red), dT (blue) and dα (green) on the interval number: a - for a healthy person, b - for a patient in a vegetative state due to anoxic brain damage, c - for a patient in a vegetative state condition due to traumatic brain injury, d - for a patient in a state of minimal consciousness due to traumatic brain injury, e - for a patient who regained consciousness after severe brain contusion
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Figure: 4. Diagrams of the amplitude range of R-peaks: a - for healthy subjects, b - for patients of the main group
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Figure: 5. Values of the dispersion of the amplitude of the R wave for different categories of patients
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Figure: 6. Diagrams of the range of values of the coefficients of asymmetry (a) and kurtosis (b) for patients of the main group and healthy subjects
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Figure: 7. The structure of the neural network
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Figure: 8. Dependence of the classification accuracy of the neural network on the number of training epochs for training (blue) and test (orange) data
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