22 research outputs found

    Design of non-linear filter in the problem of structural identification of biomedical signals with locally concentrated properties

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    In this paper we propose a generalized method of structural identification of biomedical signals with locally concentrated properties using a digital non-linear filter. The experimental verification of the detecting function was performed by using different ways to describe the model of the desired class of structural elements

    Development of method of matched morphological filtering of biomedical signals and images

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    Formalized approach to the analysis of biomedical signals and images with locally concentrated features is developed on the basis of matched morphological filtering taking into account the useful signal models that allowed generalizing the existing methods of digital processing and analysis of biomedical signals and images with locally concentrated features. The proposed matched morphological filter has been adapted to solve such problems as localization of the searched structural elements on biomedical signals with locally concentrated features, estimation of the irregular background aimed at the visualization quality improving of biological objects on X-ray biomedical images, pathologic structures selection on mammogram. The efficiency of the proposed methods of matched morphological filtration of biomedical signals and images with locally concentrated features is proved by experiments

    Π‘ΠΈΠ½Ρ‚Π΅Π· ΠΊΠΎΠΌΠ±ΠΈΠ½ΠΈΡ€ΠΎΠ²Π°Π½Π½ΠΎΠ³ΠΎ Ρ€Π΅ΡˆΠ°ΡŽΡ‰Π΅Π³ΠΎ ΠΏΡ€Π°Π²ΠΈΠ»Π° (РП) Π² ΠΊΠΎΠΌΠΏΡŒΡŽΡ‚Π΅Ρ€Π½Ρ‹Ρ… систСмах мСдицинской диагностики

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    Π—Π°ΠΏΡ€ΠΎΠΏΠΎΠ½ΠΎΠ²Π°Π½ΠΎ ΠΌΠ΅Ρ‚ΠΎΠ΄ синтСзу ΠΊΠΎΠΌΠ±Ρ–Π½ΠΎΠ²Π°Π½ΠΎΠ³ΠΎ Π’ΠŸ Ρƒ ΠΊΠΎΠΌΠΏβ€™ΡŽΡ‚Π΅Ρ€Π½ΠΈΡ… систСмах ΠΌΠ΅Π΄ΠΈΡ‡Π½ΠΎΡ— діагностики ΠΏΡ€ΠΈ Π²Π·Π°Ρ”ΠΌΠΎΠ΄Ρ–Ρ— Ρ–Ρ”Ρ€Π°Ρ€Ρ…Ρ–Ρ‡Π½ΠΈΡ… структур діагностичних ΠΎΠ·Π½Π°ΠΊ Ρ– станів, Ρ‰ΠΎ Π΄Ρ–Π°Π³Π½ΠΎΡΡ‚ΡƒΡŽΡ‚ΡŒΡΡ Π½Π° основі Π°Π½Π°Π»Ρ–Π·Ρƒ Π°ΠΏΡ€Ρ–ΠΎΡ€Π½ΠΈΡ… ΡƒΠΌΠΎΠ²Π½ΠΈΡ… ймовірностСй, Ρ—Ρ…Π½Ρ–Ρ… нСвизначСностСй Ρ‚Π° СкспСртних ΠΎΡ†Ρ–Π½ΠΎΠΊ структур симптомокомплСксів.A method for synthesis of combined decision rule in computer systems of medical diagnostics through interaction of hierarchical structures of diagnostic signs and conditions is proposed on the basis of analysis of a priori conditional probabilities, their uncertainties, and expert estimates of the structures of symptomatic complexes.ΠŸΡ€Π΅Π΄Π»ΠΎΠΆΠ΅Π½ ΠΌΠ΅Ρ‚ΠΎΠ΄ синтСза ΠΊΠΎΠΌΠ±ΠΈΠ½ΠΈΡ€ΠΎΠ²Π°Π½Π½ΠΎΠ³ΠΎ Ρ€Π΅ΡˆΠ°ΡŽΡ‰Π΅Π³ΠΎ ΠΏΡ€Π°Π²ΠΈΠ»Π° Π² ΠΊΠΎΠΌΠΏΡŒΡŽΡ‚Π΅Ρ€Π½Ρ‹Ρ… систСмах мСдицинской диагностики ΠΏΡ€ΠΈ взаимодСйствии иСрархичСских структур диагностичСских ΠΏΡ€ΠΈΠ·Π½Π°ΠΊΠΎΠ² ΠΈ диагностируСмых состояний Π½Π° основС Π°Π½Π°Π»ΠΈΠ·Π° Π°ΠΏΡ€ΠΈΠΎΡ€Π½Ρ‹Ρ… условных вСроятностСй, ΠΈΡ… нСопрСдСлСнностСй ΠΈ экспСртных ΠΎΡ†Π΅Π½ΠΎΠΊ структур симптомокомплСксов

    Π‘ΠΈΠ½Ρ‚Π΅Π· ΠΊΠΎΠΌΠ±Ρ–Π½ΠΎΠ²Π°Π½ΠΎΠ³ΠΎ Π²ΠΈΡ€Ρ–ΡˆΠ°Π»ΡŒΠ½ΠΎΠ³ΠΎ ΠΏΡ€Π°Π²ΠΈΠ»Π° (Π’ΠŸ) Ρƒ ΠΊΠΎΠΌΠΏβ€™ΡŽΡ‚Π΅Ρ€Π½ΠΈΡ… систСмах ΠΌΠ΅Π΄ΠΈΡ‡Π½ΠΎΡ— діагностики

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    A method for synthesis of combined decision rule in computer systems of medical diagnostics through interaction of hierarchical structures of diagnostic signs and conditions is proposed on the basis of analysis of a priori conditional probabilities, their uncertainties, and expert estimates of the structures of symptomatic complexes.ΠŸΡ€Π΅Π΄Π»ΠΎΠΆΠ΅Π½ ΠΌΠ΅Ρ‚ΠΎΠ΄ синтСза ΠΊΠΎΠΌΠ±ΠΈΠ½ΠΈΡ€ΠΎΠ²Π°Π½Π½ΠΎΠ³ΠΎ Ρ€Π΅ΡˆΠ°ΡŽΡ‰Π΅Π³ΠΎ ΠΏΡ€Π°Π²ΠΈΠ»Π° Π² ΠΊΠΎΠΌΠΏΡŒΡŽΡ‚Π΅Ρ€Π½Ρ‹Ρ… систСмах мСдицинской диагностики ΠΏΡ€ΠΈ взаимодСйствии иСрархичСских структур диагностичСских ΠΏΡ€ΠΈΠ·Π½Π°ΠΊΠΎΠ² ΠΈ диагностируСмых состояний Π½Π° основС Π°Π½Π°Π»ΠΈΠ·Π° Π°ΠΏΡ€ΠΈΠΎΡ€Π½Ρ‹Ρ… условных вСроятностСй, ΠΈΡ… нСопрСдСлСнностСй ΠΈ экспСртных ΠΎΡ†Π΅Π½ΠΎΠΊ структур симптомокомплСксов.Π—Π°ΠΏΡ€ΠΎΠΏΠΎΠ½ΠΎΠ²Π°Π½ΠΎ ΠΌΠ΅Ρ‚ΠΎΠ΄ синтСзу ΠΊΠΎΠΌΠ±Ρ–Π½ΠΎΠ²Π°Π½ΠΎΠ³ΠΎ Π’ΠŸ Ρƒ ΠΊΠΎΠΌΠΏβ€™ΡŽΡ‚Π΅Ρ€Π½ΠΈΡ… систСмах ΠΌΠ΅Π΄ΠΈΡ‡Π½ΠΎΡ— діагностики ΠΏΡ€ΠΈ Π²Π·Π°Ρ”ΠΌΠΎΠ΄Ρ–Ρ— Ρ–Ρ”Ρ€Π°Ρ€Ρ…Ρ–Ρ‡Π½ΠΈΡ… структур діагностичних ΠΎΠ·Π½Π°ΠΊ Ρ– станів, Ρ‰ΠΎ Π΄Ρ–Π°Π³Π½ΠΎΡΡ‚ΡƒΡŽΡ‚ΡŒΡΡ Π½Π° основі Π°Π½Π°Π»Ρ–Π·Ρƒ Π°ΠΏΡ€Ρ–ΠΎΡ€Π½ΠΈΡ… ΡƒΠΌΠΎΠ²Π½ΠΈΡ… ймовірностСй, Ρ—Ρ…Π½Ρ–Ρ… нСвизначСностСй Ρ‚Π° СкспСртних ΠΎΡ†Ρ–Π½ΠΎΠΊ структур симптомокомплСксів

    Π Π°Π·Ρ€Π°Π±ΠΎΡ‚ΠΊΠ° систСмы Π°Π»ΡŒΡ‚Π΅Ρ€Π½Π°Ρ‚ΠΈΠ²Π½Ρ‹Ρ… диагностичСских ΠΏΡ€ΠΈΠ·Π½Π°ΠΊΠΎΠ² Π² кардиологичСских систСмах ΠΏΠΎΠ΄Π΄Π΅Ρ€ΠΆΠΊΠΈ принятия Ρ€Π΅ΡˆΠ΅Π½ΠΈΠΉ

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    The trend towards an increase in the production of Ukrainian digital electrocardiographic telemetry systems such as transtelephonic digital 12-channel electrocardiograph complex "Telecard" identified the need to create intelligent automated cardiac decision support systems. The basis of these systems is the morphologic analysis of electrocardiograms, which represent biomedical signals with locally concentrated features. The system of alternative diagnostic features based on the method proposed by the authors of the morphological analysis of biomedical signals with locally concentrated features to provide additional graphical information in the diagnosis of one of the most common cardiac arrhythmias - ventricular arrhythmia is developed. Representation of the electrocardiogram in two-dimensional space of alternative features, as well as hodograph is proposed. Differences between the ECG-hodographs for normal ECG and ECG with different arrhythmias of right and left ventricles, as well as multifocal ventricular arrhythmia are analyzed. It was found that a graphical representation of an electrocardiogram in the alternative feature space allows the physician to visually perform the classification of different types of ventricular arrhythmia, which in combination with the classical analysis of ECG on the time axis increases the reliability of diagnostics.Π Π°Π·Ρ€Π°Π±ΠΎΡ‚Π°Π½Π° систСма Π°Π»ΡŒΡ‚Π΅Ρ€Π½Π°Ρ‚ΠΈΠ²Π½Ρ‹Ρ… диагностичСских ΠΏΡ€ΠΈΠ·Π½Π°ΠΊΠΎΠ² Π½Π° основС ΠΌΠ΅Ρ‚ΠΎΠ΄Π° морфологичСского Π°Π½Π°Π»ΠΈΠ·Π° биомСдицинских сигналов с локально сосрСдоточСнными ΠΏΡ€ΠΈΠ·Π½Π°ΠΊΠ°ΠΌΠΈ с Ρ†Π΅Π»ΡŒΡŽ прСдоставлСния Π΄ΠΎΠΏΠΎΠ»Π½ΠΈΡ‚Π΅Π»ΡŒΠ½ΠΎΠΉ ΠΈΠ½Ρ„ΠΎΡ€ΠΌΠ°Ρ†ΠΈΠΈ ΠΏΡ€ΠΈ диагностикС ΠΆΠ΅Π»ΡƒΠ΄ΠΎΡ‡ΠΊΠΎΠ²ΠΎΠΉ экстрасистолии. ΠŸΡ€Π΅Π΄Π»ΠΎΠΆΠ΅Π½ΠΎ прСдставлСниС элСктрокардиограммы Π² Π°Π»ΡŒΡ‚Π΅Ρ€Π½Π°Ρ‚ΠΈΠ²Π½ΠΎΠΌ пространствС ΠΏΡ€ΠΈΠ·Π½Π°ΠΊΠΎΠ² Π² Π²ΠΈΠ΄Π΅ Π³ΠΎΠ΄ΠΎΠ³Ρ€Π°Ρ„Π°. ΠŸΡ€ΠΎΠ°Π½Π°Π»ΠΈΠ·ΠΈΡ€ΠΎΠ²Π°Π½Ρ‹ отличия Π³ΠΎΠ΄ΠΎΠ³Ρ€Π°Ρ„ΠΎΠ² для Π½ΠΎΡ€ΠΌΠ°Π»ΡŒΠ½ΠΎΠΉ элСктрокардиограммы ΠΈ элСктрокардиограмм с Ρ€Π°Π·Π»ΠΈΡ‡Π½Ρ‹ΠΌΠΈ Π²ΠΈΠ΄Π°ΠΌΠΈ ΠΆΠ΅Π»ΡƒΠ΄ΠΎΡ‡ΠΊΠΎΠ²ΠΎΠΉ экстрасистолии

    The designing of non-linear filter in the problem of structure identification of biomedical signals with locally focused signs

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    Π ΠΎΠ±ΠΎΡ‚Ρƒ спрямовано Π½Π° підвищСння якості структурної Ρ–Π΄Π΅Π½Ρ‚ΠΈΡ„Ρ–ΠΊΠ°Ρ†Ρ–Ρ— Π±Ρ–ΠΎΠΌΠ΅Π΄ΠΈΡ‡Π½ΠΈΡ… сигналів Π· локально зосСрСдТСними ΠΎΠ·Π½Π°ΠΊΠ°ΠΌΠΈ Π·Π° Ρ€Π°Ρ…ΡƒΠ½ΠΎΠΊ Ρ€ΠΎΠ·Ρ€ΠΎΠ±ΠΊΠΈ Π½ΠΎΠ²ΠΈΡ… ΠΌΠ΅Ρ‚ΠΎΠ΄Ρ–Π² Π²ΠΈΡ€Ρ–ΡˆΠ΅Π½Π½Ρ поставлСної Π·Π°Π΄Π°Ρ‡Ρ–. Розглянуто ΠΏΡ€ΠΎΠ±Π»Π΅ΠΌΡƒ проСктування Ρ–Π½Ρ‚Π΅Π»Π΅ΠΊΡ‚ΡƒΠ°Π»ΡŒΠ½ΠΈΡ… ΠΊΠΎΠΌΠΏβ€™ΡŽΡ‚Π΅Ρ€Π½ΠΈΡ… ΠΊΠ°Ρ€Π΄Ρ–ΠΎΠ»ΠΎΠ³Ρ–Ρ‡Π½ΠΈΡ… систСм ΠΏΡ–Π΄Ρ‚Ρ€ΠΈΠΌΠΊΠΈ прийняття Ρ€Ρ–ΡˆΠ΅Π½ΡŒ Ρ‚Π° ΡΡ„ΠΎΡ€ΠΌΡƒΠ»ΡŒΠΎΠ²Π°Π½ΠΎ основні Π΅Ρ‚Π°ΠΏΠΈ ΠΎΠ±Ρ€ΠΎΠ±ΠΊΠΈ Π±Ρ–ΠΎΠΌΠ΅Π΄ΠΈΡ‡Π½ΠΈΡ… сигналів Π· локально зосСрСдТСними ΠΎΠ·Π½Π°ΠΊΠ°ΠΌΠΈ. Π—Π°ΠΏΡ€ΠΎΠΏΠΎΠ½ΠΎΠ²Π°Π½ΠΎ ΡƒΠ·Π°Π³Π°Π»ΡŒΠ½Π΅Π½ΠΈΠΉ ΠΌΠ΅Ρ‚ΠΎΠ΄ структурної Ρ–Π΄Π΅Π½Ρ‚ΠΈΡ„Ρ–ΠΊΠ°Ρ†Ρ–Ρ— Π±Ρ–ΠΎΠΌΠ΅Π΄ΠΈΡ‡Π½ΠΈΡ… сигналів Π· локально зосСрСдТСними ΠΎΠ·Π½Π°ΠΊΠ°ΠΌΠΈ Π·Π° допомогою Ρ†ΠΈΡ„Ρ€ΠΎΠ²ΠΎΠ³ΠΎ Π½Π΅Π»Ρ–Π½Ρ–ΠΉΠ½ΠΎΠ³ΠΎ Ρ„Ρ–Π»ΡŒΡ‚Ρ€Π°. ΠŸΡ€ΠΎΠ²Π΅Π΄Π΅Π½ΠΎ дослідТСння ΠΏΠ°Ρ€Π°ΠΌΠ΅Ρ‚Ρ€Ρ–Π² Π½Π΅Π»Ρ–Π½Ρ–ΠΉΠ½ΠΎΠ³ΠΎ Ρ„Ρ–Π»ΡŒΡ‚Ρ€Π° Π² Π·Π°Π΄Π°Ρ‡Ρ– структурної Ρ–Π΄Π΅Π½Ρ‚ΠΈΡ„Ρ–ΠΊΠ°Ρ†Ρ–Ρ— Π±Ρ–ΠΎΠΌΠ΅Π΄ΠΈΡ‡Π½ΠΈΡ… сигналів Π· локально зосСрСдТСними ΠΎΠ·Π½Π°ΠΊΠ°ΠΌΠΈ, Π²ΠΈΠΊΠΎΠ½Π°Π½ΠΎ синтСз ΠΊΡ€ΠΈΡ‚Π΅Ρ€Ρ–ΡŽ якості структурної Ρ–Π΄Π΅Π½Ρ‚ΠΈΡ„Ρ–ΠΊΠ°Ρ†Ρ–Ρ— Π½Π° основі спроСктованого Π½Π΅Π»Ρ–Π½Ρ–ΠΉΠ½ΠΎΠ³ΠΎ Ρ„Ρ–Π»ΡŒΡ‚Ρ€Π°, Π° Ρ‚Π°ΠΊΠΎΠΆ Π²ΠΈΠΊΠΎΠ½Π°Π½ΠΎ Π΅ΠΊΡΠΏΠ΅Ρ€ΠΈΠΌΠ΅Π½Ρ‚Π°Π»ΡŒΠ½Ρƒ ΠΏΠ΅Ρ€Π΅Π²Ρ–Ρ€ΠΊΡƒ якості структурної Ρ–Π΄Π΅Π½Ρ‚ΠΈΡ„Ρ–ΠΊΠ°Ρ†Ρ–Ρ— ΠΏΡ€ΠΈ Π·Π°Π²Π΄Π°Π½Π½Ρ– Ρ€Ρ–Π·Π½ΠΈΡ… ΠΏΠ°Ρ€Π°ΠΌΠ΅Ρ‚Ρ€Ρ–Π² Π½Π΅Π»Ρ–Π½Ρ–ΠΉΠ½ΠΎΠ³ΠΎ Ρ„Ρ–Π»ΡŒΡ‚Ρ€Π°. Π—Ρ€ΠΎΠ±Π»Π΅Π½ΠΎ висновки Ρ‰ΠΎΠ΄ΠΎ СфСктивності застосування Ρ€Ρ–Π·Π½ΠΈΡ… ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ корисного сигналу для структурної Ρ–Π΄Π΅Π½Ρ‚ΠΈΡ„Ρ–ΠΊΠ°Ρ†Ρ–Ρ— Π±Ρ–ΠΎΠΌΠ΅Π΄ΠΈΡ‡Π½ΠΈΡ… сигналів Π· локально зосСрСдТСними ΠΎΠ·Π½Π°ΠΊΠ°ΠΌΠΈ.This research is aimed to improve the quality of structural identification of biomedical signals with locally focused signs through the development of new methods for solving this problem. The problem of designing of intelligent computer decision support systems in cardiology is considered in this research. Also, the main stages of processing of biomedical signals with locally focused signs are formulated. Generalized method of structural identification of biomedical signals with locally focused signs using a digital non-linear filter is proposed. Analysis of the non-linear filter parameters in the problem of structural identification of biomedical signals with locally focused signs is conducted, synthesis of quality criteria of structural identification based on the designed non-linear filter is completed, the experimental verification of the quality of structural identification by setting various parameters of the nonlinear filter is implemented. Conclusions about the effectiveness of different models of the desired signal for the structural identification of biomedical signals with locally focused signs are made.Π Π°Π±ΠΎΡ‚Π° Π½Π°ΠΏΡ€Π°Π²Π»Π΅Π½Π° Π½Π° ΠΏΠΎΠ²Ρ‹ΡˆΠ΅Π½ΠΈΠ΅ качСства структурной ΠΈΠ΄Π΅Π½Ρ‚ΠΈΡ„ΠΈΠΊΠ°Ρ†ΠΈΠΈ биомСдицинских сигналов с локально сосрСдоточСнными ΠΏΡ€ΠΈΠ·Π½Π°ΠΊΠ°ΠΌΠΈ Π·Π° счСт Ρ€Π°Π·Ρ€Π°Π±ΠΎΡ‚ΠΊΠΈ Π½ΠΎΠ²Ρ‹Ρ… ΠΌΠ΅Ρ‚ΠΎΠ΄ΠΎΠ² Ρ€Π΅ΡˆΠ΅Π½ΠΈΡ поставлСнной Π·Π°Π΄Π°Ρ‡ΠΈ. РассмотрСна ΠΏΡ€ΠΎΠ±Π»Π΅ΠΌΠ° проСктирования ΠΈΠ½Ρ‚Π΅Π»Π»Π΅ΠΊΡ‚ΡƒΠ°Π»ΡŒΠ½Ρ‹Ρ… ΠΊΠΎΠΌΠΏΡŒΡŽΡ‚Π΅Ρ€Π½Ρ‹Ρ… кардиологичСских систСм ΠΏΠΎΠ΄Π΄Π΅Ρ€ΠΆΠΊΠΈ принятия Ρ€Π΅ΡˆΠ΅Π½ΠΈΠΉ ΠΈ сформулированы основныС этапы ΠΎΠ±Ρ€Π°Π±ΠΎΡ‚ΠΊΠΈ биомСдицинских сигналов с локально сосрСдоточСнными ΠΏΡ€ΠΈΠ·Π½Π°ΠΊΠ°ΠΌΠΈ. ΠŸΡ€Π΅Π΄Π»ΠΎΠΆΠ΅Π½ ΠΎΠ±ΠΎΠ±Ρ‰Π΅Π½Π½Ρ‹ΠΉ ΠΌΠ΅Ρ‚ΠΎΠ΄ структурной ΠΈΠ΄Π΅Π½Ρ‚ΠΈΡ„ΠΈΠΊΠ°Ρ†ΠΈΠΈ биомСдицинских сигналов с локально сосрСдоточСнными ΠΏΡ€ΠΈΠ·Π½Π°ΠΊΠ°ΠΌΠΈ с ΠΏΠΎΠΌΠΎΡ‰ΡŒΡŽ Ρ†ΠΈΡ„Ρ€ΠΎΠ²ΠΎΠ³ΠΎ Π½Π΅Π»ΠΈΠ½Π΅ΠΉΠ½ΠΎΠ³ΠΎ Ρ„ΠΈΠ»ΡŒΡ‚Ρ€Π°. ΠŸΡ€ΠΎΠ²Π΅Π΄Π΅Π½ΠΎ исслСдованиС ΠΏΠ°Ρ€Π°ΠΌΠ΅Ρ‚Ρ€ΠΎΠ² Π½Π΅Π»ΠΈΠ½Π΅ΠΉΠ½ΠΎΠ³ΠΎ Ρ„ΠΈΠ»ΡŒΡ‚Ρ€Π° Π² Π·Π°Π΄Π°Ρ‡Π΅ структурной ΠΈΠ΄Π΅Π½Ρ‚ΠΈΡ„ΠΈΠΊΠ°Ρ†ΠΈΠΈ биомСдицинских сигналов с локально сосрСдоточСнными ΠΏΡ€ΠΈΠ·Π½Π°ΠΊΠ°ΠΌΠΈ, Π²Ρ‹ΠΏΠΎΠ»Π½Π΅Π½ синтСз критСрия качСства структурной ΠΈΠ΄Π΅Π½Ρ‚ΠΈΡ„ΠΈΠΊΠ°Ρ†ΠΈΠΈ Π½Π° основС спроСктированного Π½Π΅Π»ΠΈΠ½Π΅ΠΉΠ½ΠΎΠ³ΠΎ Ρ„ΠΈΠ»ΡŒΡ‚Ρ€Π°, Π° Ρ‚Π°ΠΊΠΆΠ΅ Π²Ρ‹ΠΏΠΎΠ»Π½Π΅Π½Π° ΡΠΊΡΠΏΠ΅Ρ€ΠΈΠΌΠ΅Π½Ρ‚Π°Π»ΡŒΠ½Π°Ρ ΠΏΡ€ΠΎΠ²Π΅Ρ€ΠΊΠ° качСства структурной ΠΈΠ΄Π΅Π½Ρ‚ΠΈΡ„ΠΈΠΊΠ°Ρ†ΠΈΠΈ ΠΏΡ€ΠΈ Π·Π°Π΄Π°Π½ΠΈΠΈ Ρ€Π°Π·Π»ΠΈΡ‡Π½Ρ‹Ρ… ΠΏΠ°Ρ€Π°ΠΌΠ΅Ρ‚Ρ€ΠΎΠ² Π½Π΅Π»ΠΈΠ½Π΅ΠΉΠ½ΠΎΠ³ΠΎ Ρ„ΠΈΠ»ΡŒΡ‚Ρ€Π°. Π‘Π΄Π΅Π»Π°Π½Ρ‹ Π²Ρ‹Π²ΠΎΠ΄Ρ‹ ΠΎΠ± эффСктивности примСнСния Ρ€Π°Π·Π»ΠΈΡ‡Π½Ρ‹Ρ… ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ ΠΏΠΎΠ»Π΅Π·Π½ΠΎΠ³ΠΎ сигнала для структурной ΠΈΠ΄Π΅Π½Ρ‚ΠΈΡ„ΠΈΠΊΠ°Ρ†ΠΈΠΈ биомСдицинских сигналов с локально сосрСдоточСнными ΠΏΡ€ΠΈΠ·Π½Π°ΠΊΠ°ΠΌΠΈ

    The Association of Sarcopenia and Osteoporosis and Their Role in Falls and Fractures (Literature Review)

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    The progressive and generalized loss of skeletal muscle mass and strength leads to sarcopenia in elderly people. A new geriatric syndrome has been revealed – osteosarcopenia (osteosarcoporosis), which combines low bone mineral density with reduced muscle mass, strength and functional activity. The review presents data on the peculiarities of manifestation of these syndromes, the mechanisms of which are multifactorial and continue to be investigated. They are associated with genetic factors, lifestyle – lack of physical activity and malnutrition. The pathogenesis of sarcopenia involves mechanisms of chronic inflammation, changes in endocrine function, disturbance of neuromuscular connections and low reparation level. Sarcopenia correlates with low quality of life, disability, and death. The review analyzes the prevalence of sarcopenia which increases with age. However, there are conflicting results in the populations, which may be related to different clinical conditions, patient area, lifestyle and the use of different assessment criteria. The analysis of sarcopenia prevalence in men and women showed ambiguous results related to the studied population, involvement of different age groups of patients, different evaluation methods. Metabolic disorders in muscular and bone tissues were summarized on the basis of the analysis of the cross-influence of regulatory factors and metabolism products of these tissues; a close metabolic and functional association between them was shown. Fat infiltration of atrophied muscles and bone marrow is common in patients with sarcopenia and osteosarcoporosis, which affects muscle and bone tissue. Lipotoxicity and local inflammation stimulate the biosynthesis of pro-inflammatory cytokines. Literature analysis has shown controversial data on the association of sarcopenia and osteosarcopenia with falls and fractures, but based on meta-analysis data, which include an extensive body of information, it should be noted that individuals with sarcopenia and osteosarcopenia are more at risk of falls and fractures and require special special attention. The most common fracture in osteosarcopenia is the hip fracture
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