22 research outputs found
Design of non-linear filter in the problem of structural identification of biomedical signals with locally concentrated properties
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
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‘ΠΈΠ½ΡΠ΅Π· ΠΊΠΎΠΌΠ±ΠΈΠ½ΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠ³ΠΎ ΡΠ΅ΡΠ°ΡΡΠ΅Π³ΠΎ ΠΏΡΠ°Π²ΠΈΠ»Π° (Π Π) Π² ΠΊΠΎΠΌΠΏΡΡΡΠ΅ΡΠ½ΡΡ ΡΠΈΡΡΠ΅ΠΌΠ°Ρ ΠΌΠ΅Π΄ΠΈΡΠΈΠ½ΡΠΊΠΎΠΉ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΠΈ
ΠΠ°ΠΏΡΠΎΠΏΠΎΠ½ΠΎΠ²Π°Π½ΠΎ ΠΌΠ΅ΡΠΎΠ΄ ΡΠΈΠ½ΡΠ΅Π·Ρ ΠΊΠΎΠΌΠ±ΡΠ½ΠΎΠ²Π°Π½ΠΎΠ³ΠΎ ΠΠ Ρ ΠΊΠΎΠΌΠΏβΡΡΠ΅ΡΠ½ΠΈΡ
ΡΠΈΡΡΠ΅ΠΌΠ°Ρ
ΠΌΠ΅Π΄ΠΈΡΠ½ΠΎΡ Π΄ΡΠ°Π³Π½ΠΎΡΡΠΈΠΊΠΈ ΠΏΡΠΈ Π²Π·Π°ΡΠΌΠΎΠ΄ΡΡ ΡΡΡΠ°ΡΡ
ΡΡΠ½ΠΈΡ
ΡΡΡΡΠΊΡΡΡ Π΄ΡΠ°Π³Π½ΠΎΡΡΠΈΡΠ½ΠΈΡ
ΠΎΠ·Π½Π°ΠΊ Ρ ΡΡΠ°Π½ΡΠ², ΡΠΎ Π΄ΡΠ°Π³Π½ΠΎΡΡΡΡΡΡΡΡ Π½Π° ΠΎΡΠ½ΠΎΠ²Ρ Π°Π½Π°Π»ΡΠ·Ρ Π°ΠΏΡΡΠΎΡΠ½ΠΈΡ
ΡΠΌΠΎΠ²Π½ΠΈΡ
ΠΉΠΌΠΎΠ²ΡΡΠ½ΠΎΡΡΠ΅ΠΉ, ΡΡ
Π½ΡΡ
Π½Π΅Π²ΠΈΠ·Π½Π°ΡΠ΅Π½ΠΎΡΡΠ΅ΠΉ ΡΠ° Π΅ΠΊΡΠΏΠ΅ΡΡΠ½ΠΈΡ
ΠΎΡΡΠ½ΠΎΠΊ ΡΡΡΡΠΊΡΡΡ ΡΠΈΠΌΠΏΡΠΎΠΌΠΎΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡΡΠ².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.ΠΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½ ΠΌΠ΅ΡΠΎΠ΄ ΡΠΈΠ½ΡΠ΅Π·Π° ΠΊΠΎΠΌΠ±ΠΈΠ½ΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠ³ΠΎ ΡΠ΅ΡΠ°ΡΡΠ΅Π³ΠΎ ΠΏΡΠ°Π²ΠΈΠ»Π° Π² ΠΊΠΎΠΌΠΏΡΡΡΠ΅ΡΠ½ΡΡ
ΡΠΈΡΡΠ΅ΠΌΠ°Ρ
ΠΌΠ΅Π΄ΠΈΡΠΈΠ½ΡΠΊΠΎΠΉ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΠΈ ΠΏΡΠΈ Π²Π·Π°ΠΈΠΌΠΎΠ΄Π΅ΠΉΡΡΠ²ΠΈΠΈ ΠΈΠ΅ΡΠ°ΡΡ
ΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΡΡΡΠΊΡΡΡ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΏΡΠΈΠ·Π½Π°ΠΊΠΎΠ² ΠΈ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΡΡΠ΅ΠΌΡΡ
ΡΠΎΡΡΠΎΡΠ½ΠΈΠΉ Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ Π°Π½Π°Π»ΠΈΠ·Π° Π°ΠΏΡΠΈΠΎΡΠ½ΡΡ
ΡΡΠ»ΠΎΠ²Π½ΡΡ
Π²Π΅ΡΠΎΡΡΠ½ΠΎΡΡΠ΅ΠΉ, ΠΈΡ
Π½Π΅ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½Π½ΠΎΡΡΠ΅ΠΉ ΠΈ ΡΠΊΡΠΏΠ΅ΡΡΠ½ΡΡ
ΠΎΡΠ΅Π½ΠΎΠΊ ΡΡΡΡΠΊΡΡΡ ΡΠΈΠΌΠΏΡΠΎΠΌΠΎΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡΠΎΠ²
Π‘ΠΈΠ½ΡΠ΅Π· ΠΊΠΎΠΌΠ±ΡΠ½ΠΎΠ²Π°Π½ΠΎΠ³ΠΎ Π²ΠΈΡΡΡΠ°Π»ΡΠ½ΠΎΠ³ΠΎ ΠΏΡΠ°Π²ΠΈΠ»Π° (ΠΠ) Ρ ΠΊΠΎΠΌΠΏβΡΡΠ΅ΡΠ½ΠΈΡ ΡΠΈΡΡΠ΅ΠΌΠ°Ρ ΠΌΠ΅Π΄ΠΈΡΠ½ΠΎΡ Π΄ΡΠ°Π³Π½ΠΎΡΡΠΈΠΊΠΈ
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.ΠΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½ ΠΌΠ΅ΡΠΎΠ΄ ΡΠΈΠ½ΡΠ΅Π·Π° ΠΊΠΎΠΌΠ±ΠΈΠ½ΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠ³ΠΎ ΡΠ΅ΡΠ°ΡΡΠ΅Π³ΠΎ ΠΏΡΠ°Π²ΠΈΠ»Π° Π² ΠΊΠΎΠΌΠΏΡΡΡΠ΅ΡΠ½ΡΡ
ΡΠΈΡΡΠ΅ΠΌΠ°Ρ
ΠΌΠ΅Π΄ΠΈΡΠΈΠ½ΡΠΊΠΎΠΉ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΠΈ ΠΏΡΠΈ Π²Π·Π°ΠΈΠΌΠΎΠ΄Π΅ΠΉΡΡΠ²ΠΈΠΈ ΠΈΠ΅ΡΠ°ΡΡ
ΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΡΡΡΠΊΡΡΡ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΏΡΠΈΠ·Π½Π°ΠΊΠΎΠ² ΠΈ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΡΡΠ΅ΠΌΡΡ
ΡΠΎΡΡΠΎΡΠ½ΠΈΠΉ Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ Π°Π½Π°Π»ΠΈΠ·Π° Π°ΠΏΡΠΈΠΎΡΠ½ΡΡ
ΡΡΠ»ΠΎΠ²Π½ΡΡ
Π²Π΅ΡΠΎΡΡΠ½ΠΎΡΡΠ΅ΠΉ, ΠΈΡ
Π½Π΅ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½Π½ΠΎΡΡΠ΅ΠΉ ΠΈ ΡΠΊΡΠΏΠ΅ΡΡΠ½ΡΡ
ΠΎΡΠ΅Π½ΠΎΠΊ ΡΡΡΡΠΊΡΡΡ ΡΠΈΠΌΠΏΡΠΎΠΌΠΎΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡΠΎΠ².ΠΠ°ΠΏΡΠΎΠΏΠΎΠ½ΠΎΠ²Π°Π½ΠΎ ΠΌΠ΅ΡΠΎΠ΄ ΡΠΈΠ½ΡΠ΅Π·Ρ ΠΊΠΎΠΌΠ±ΡΠ½ΠΎΠ²Π°Π½ΠΎΠ³ΠΎ ΠΠ Ρ ΠΊΠΎΠΌΠΏβΡΡΠ΅ΡΠ½ΠΈΡ
ΡΠΈΡΡΠ΅ΠΌΠ°Ρ
ΠΌΠ΅Π΄ΠΈΡΠ½ΠΎΡ Π΄ΡΠ°Π³Π½ΠΎΡΡΠΈΠΊΠΈ ΠΏΡΠΈ Π²Π·Π°ΡΠΌΠΎΠ΄ΡΡ ΡΡΡΠ°ΡΡ
ΡΡΠ½ΠΈΡ
ΡΡΡΡΠΊΡΡΡ Π΄ΡΠ°Π³Π½ΠΎΡΡΠΈΡΠ½ΠΈΡ
ΠΎΠ·Π½Π°ΠΊ Ρ ΡΡΠ°Π½ΡΠ², ΡΠΎ Π΄ΡΠ°Π³Π½ΠΎΡΡΡΡΡΡΡΡ Π½Π° ΠΎΡΠ½ΠΎΠ²Ρ Π°Π½Π°Π»ΡΠ·Ρ Π°ΠΏΡΡΠΎΡΠ½ΠΈΡ
ΡΠΌΠΎΠ²Π½ΠΈΡ
ΠΉΠΌΠΎΠ²ΡΡΠ½ΠΎΡΡΠ΅ΠΉ, ΡΡ
Π½ΡΡ
Π½Π΅Π²ΠΈΠ·Π½Π°ΡΠ΅Π½ΠΎΡΡΠ΅ΠΉ ΡΠ° Π΅ΠΊΡΠΏΠ΅ΡΡΠ½ΠΈΡ
ΠΎΡΡΠ½ΠΎΠΊ ΡΡΡΡΠΊΡΡΡ ΡΠΈΠΌΠΏΡΠΎΠΌΠΎΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡΡΠ²
Π Π°Π·ΡΠ°Π±ΠΎΡΠΊΠ° ΡΠΈΡΡΠ΅ΠΌΡ Π°Π»ΡΡΠ΅ΡΠ½Π°ΡΠΈΠ²Π½ΡΡ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΡΠ΅ΡΠΊΠΈΡ ΠΏΡΠΈΠ·Π½Π°ΠΊΠΎΠ² Π² ΠΊΠ°ΡΠ΄ΠΈΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΡ ΡΠΈΡΡΠ΅ΠΌΠ°Ρ ΠΏΠΎΠ΄Π΄Π΅ΡΠΆΠΊΠΈ ΠΏΡΠΈΠ½ΡΡΠΈΡ ΡΠ΅ΡΠ΅Π½ΠΈΠΉ
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·ΡΠ°Π±ΠΎΡΠ°Π½Π° ΡΠΈΡΡΠ΅ΠΌΠ° Π°Π»ΡΡΠ΅ΡΠ½Π°ΡΠΈΠ²Π½ΡΡ
Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΏΡΠΈΠ·Π½Π°ΠΊΠΎΠ² Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ ΠΌΠ΅ΡΠΎΠ΄Π° ΠΌΠΎΡΡΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ Π°Π½Π°Π»ΠΈΠ·Π° Π±ΠΈΠΎΠΌΠ΅Π΄ΠΈΡΠΈΠ½ΡΠΊΠΈΡ
ΡΠΈΠ³Π½Π°Π»ΠΎΠ² Ρ Π»ΠΎΠΊΠ°Π»ΡΠ½ΠΎ ΡΠΎΡΡΠ΅Π΄ΠΎΡΠΎΡΠ΅Π½Π½ΡΠΌΠΈ ΠΏΡΠΈΠ·Π½Π°ΠΊΠ°ΠΌΠΈ Ρ ΡΠ΅Π»ΡΡ ΠΏΡΠ΅Π΄ΠΎΡΡΠ°Π²Π»Π΅Π½ΠΈΡ Π΄ΠΎΠΏΠΎΠ»Π½ΠΈΡΠ΅Π»ΡΠ½ΠΎΠΉ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ ΠΏΡΠΈ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΠ΅ ΠΆΠ΅Π»ΡΠ΄ΠΎΡΠΊΠΎΠ²ΠΎΠΉ ΡΠΊΡΡΡΠ°ΡΠΈΡΡΠΎΠ»ΠΈΠΈ. ΠΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½ΠΎ ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½ΠΈΠ΅ ΡΠ»Π΅ΠΊΡΡΠΎΠΊΠ°ΡΠ΄ΠΈΠΎΠ³ΡΠ°ΠΌΠΌΡ Π² Π°Π»ΡΡΠ΅ΡΠ½Π°ΡΠΈΠ²Π½ΠΎΠΌ ΠΏΡΠΎΡΡΡΠ°Π½ΡΡΠ²Π΅ ΠΏΡΠΈΠ·Π½Π°ΠΊΠΎΠ² Π² Π²ΠΈΠ΄Π΅ Π³ΠΎΠ΄ΠΎΠ³ΡΠ°ΡΠ°. ΠΡΠΎΠ°Π½Π°Π»ΠΈΠ·ΠΈΡΠΎΠ²Π°Π½Ρ ΠΎΡΠ»ΠΈΡΠΈΡ Π³ΠΎΠ΄ΠΎΠ³ΡΠ°ΡΠΎΠ² Π΄Π»Ρ Π½ΠΎΡΠΌΠ°Π»ΡΠ½ΠΎΠΉ ΡΠ»Π΅ΠΊΡΡΠΎΠΊΠ°ΡΠ΄ΠΈΠΎΠ³ΡΠ°ΠΌΠΌΡ ΠΈ ΡΠ»Π΅ΠΊΡΡΠΎΠΊΠ°ΡΠ΄ΠΈΠΎΠ³ΡΠ°ΠΌΠΌ Ρ ΡΠ°Π·Π»ΠΈΡΠ½ΡΠΌΠΈ Π²ΠΈΠ΄Π°ΠΌΠΈ ΠΆΠ΅Π»ΡΠ΄ΠΎΡΠΊΠΎΠ²ΠΎΠΉ ΡΠΊΡΡΡΠ°ΡΠΈΡΡΠΎΠ»ΠΈΠΈ
The designing of non-linear filter in the problem of structure identification of biomedical signals with locally focused signs
Π ΠΎΠ±ΠΎΡΡ ΡΠΏΡΡΠΌΠΎΠ²Π°Π½ΠΎ Π½Π° ΠΏΡΠ΄Π²ΠΈΡΠ΅Π½Π½Ρ ΡΠΊΠΎΡΡΡ ΡΡΡΡΠΊΡΡΡΠ½ΠΎΡ ΡΠ΄Π΅Π½ΡΠΈΡΡΠΊΠ°ΡΡΡ Π±ΡΠΎΠΌΠ΅Π΄ΠΈΡΠ½ΠΈΡ
ΡΠΈΠ³Π½Π°Π»ΡΠ² Π· Π»ΠΎΠΊΠ°Π»ΡΠ½ΠΎ Π·ΠΎΡΠ΅ΡΠ΅Π΄ΠΆΠ΅Π½ΠΈΠΌΠΈ ΠΎΠ·Π½Π°ΠΊΠ°ΠΌΠΈ Π·Π° ΡΠ°Ρ
ΡΠ½ΠΎΠΊ ΡΠΎΠ·ΡΠΎΠ±ΠΊΠΈ Π½ΠΎΠ²ΠΈΡ
ΠΌΠ΅ΡΠΎΠ΄ΡΠ² Π²ΠΈΡΡΡΠ΅Π½Π½Ρ ΠΏΠΎΡΡΠ°Π²Π»Π΅Π½ΠΎΡ Π·Π°Π΄Π°ΡΡ. Π ΠΎΠ·Π³Π»ΡΠ½ΡΡΠΎ ΠΏΡΠΎΠ±Π»Π΅ΠΌΡ ΠΏΡΠΎΠ΅ΠΊΡΡΠ²Π°Π½Π½Ρ ΡΠ½ΡΠ΅Π»Π΅ΠΊΡΡΠ°Π»ΡΠ½ΠΈΡ
ΠΊΠΎΠΌΠΏβΡΡΠ΅ΡΠ½ΠΈΡ
ΠΊΠ°ΡΠ΄ΡΠΎΠ»ΠΎΠ³ΡΡΠ½ΠΈΡ
ΡΠΈΡΡΠ΅ΠΌ ΠΏΡΠ΄ΡΡΠΈΠΌΠΊΠΈ ΠΏΡΠΈΠΉΠ½ΡΡΡΡ ΡΡΡΠ΅Π½Ρ ΡΠ° ΡΡΠΎΡΠΌΡΠ»ΡΠΎΠ²Π°Π½ΠΎ ΠΎΡΠ½ΠΎΠ²Π½Ρ Π΅ΡΠ°ΠΏΠΈ ΠΎΠ±ΡΠΎΠ±ΠΊΠΈ Π±ΡΠΎΠΌΠ΅Π΄ΠΈΡΠ½ΠΈΡ
ΡΠΈΠ³Π½Π°Π»ΡΠ² Π· Π»ΠΎΠΊΠ°Π»ΡΠ½ΠΎ Π·ΠΎΡΠ΅ΡΠ΅Π΄ΠΆΠ΅Π½ΠΈΠΌΠΈ ΠΎΠ·Π½Π°ΠΊΠ°ΠΌΠΈ. ΠΠ°ΠΏΡΠΎΠΏΠΎΠ½ΠΎΠ²Π°Π½ΠΎ ΡΠ·Π°Π³Π°Π»ΡΠ½Π΅Π½ΠΈΠΉ ΠΌΠ΅ΡΠΎΠ΄ ΡΡΡΡΠΊΡΡΡΠ½ΠΎΡ ΡΠ΄Π΅Π½ΡΠΈΡΡΠΊΠ°ΡΡΡ Π±ΡΠΎΠΌΠ΅Π΄ΠΈΡΠ½ΠΈΡ
ΡΠΈΠ³Π½Π°Π»ΡΠ² Π· Π»ΠΎΠΊΠ°Π»ΡΠ½ΠΎ Π·ΠΎΡΠ΅ΡΠ΅Π΄ΠΆΠ΅Π½ΠΈΠΌΠΈ ΠΎΠ·Π½Π°ΠΊΠ°ΠΌΠΈ Π·Π° Π΄ΠΎΠΏΠΎΠΌΠΎΠ³ΠΎΡ ΡΠΈΡΡΠΎΠ²ΠΎΠ³ΠΎ Π½Π΅Π»ΡΠ½ΡΠΉΠ½ΠΎΠ³ΠΎ ΡΡΠ»ΡΡΡΠ°. ΠΡΠΎΠ²Π΅Π΄Π΅Π½ΠΎ Π΄ΠΎΡΠ»ΡΠ΄ΠΆΠ΅Π½Π½Ρ ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΡΠ² Π½Π΅Π»ΡΠ½ΡΠΉΠ½ΠΎΠ³ΠΎ ΡΡΠ»ΡΡΡΠ° Π² Π·Π°Π΄Π°ΡΡ ΡΡΡΡΠΊΡΡΡΠ½ΠΎΡ ΡΠ΄Π΅Π½ΡΠΈΡΡΠΊΠ°ΡΡΡ Π±ΡΠΎΠΌΠ΅Π΄ΠΈΡΠ½ΠΈΡ
ΡΠΈΠ³Π½Π°Π»ΡΠ² Π· Π»ΠΎΠΊΠ°Π»ΡΠ½ΠΎ Π·ΠΎΡΠ΅ΡΠ΅Π΄ΠΆΠ΅Π½ΠΈΠΌΠΈ ΠΎΠ·Π½Π°ΠΊΠ°ΠΌΠΈ, Π²ΠΈΠΊΠΎΠ½Π°Π½ΠΎ ΡΠΈΠ½ΡΠ΅Π· ΠΊΡΠΈΡΠ΅ΡΡΡ ΡΠΊΠΎΡΡΡ ΡΡΡΡΠΊΡΡΡΠ½ΠΎΡ ΡΠ΄Π΅Π½ΡΠΈΡΡΠΊΠ°ΡΡΡ Π½Π° ΠΎΡΠ½ΠΎΠ²Ρ ΡΠΏΡΠΎΠ΅ΠΊΡΠΎΠ²Π°Π½ΠΎΠ³ΠΎ Π½Π΅Π»ΡΠ½ΡΠΉΠ½ΠΎΠ³ΠΎ ΡΡΠ»ΡΡΡΠ°, Π° ΡΠ°ΠΊΠΎΠΆ Π²ΠΈΠΊΠΎΠ½Π°Π½ΠΎ Π΅ΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΠ°Π»ΡΠ½Ρ ΠΏΠ΅ΡΠ΅Π²ΡΡΠΊΡ ΡΠΊΠΎΡΡΡ ΡΡΡΡΠΊΡΡΡΠ½ΠΎΡ ΡΠ΄Π΅Π½ΡΠΈΡΡΠΊΠ°ΡΡΡ ΠΏΡΠΈ Π·Π°Π²Π΄Π°Π½Π½Ρ ΡΡΠ·Π½ΠΈΡ
ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΡΠ² Π½Π΅Π»ΡΠ½ΡΠΉΠ½ΠΎΠ³ΠΎ ΡΡΠ»ΡΡΡΠ°. ΠΡΠΎΠ±Π»Π΅Π½ΠΎ Π²ΠΈΡΠ½ΠΎΠ²ΠΊΠΈ ΡΠΎΠ΄ΠΎ Π΅ΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΡΡΡ Π·Π°ΡΡΠΎΡΡΠ²Π°Π½Π½Ρ ΡΡΠ·Π½ΠΈΡ
ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ ΠΊΠΎΡΠΈΡΠ½ΠΎΠ³ΠΎ ΡΠΈΠ³Π½Π°Π»Ρ Π΄Π»Ρ ΡΡΡΡΠΊΡΡΡΠ½ΠΎΡ ΡΠ΄Π΅Π½ΡΠΈΡΡΠΊΠ°ΡΡΡ Π±ΡΠΎΠΌΠ΅Π΄ΠΈΡΠ½ΠΈΡ
ΡΠΈΠ³Π½Π°Π»ΡΠ² Π· Π»ΠΎΠΊΠ°Π»ΡΠ½ΠΎ Π·ΠΎΡΠ΅ΡΠ΅Π΄ΠΆΠ΅Π½ΠΈΠΌΠΈ 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±ΠΎΡΠ° Π½Π°ΠΏΡΠ°Π²Π»Π΅Π½Π° Π½Π° ΠΏΠΎΠ²ΡΡΠ΅Π½ΠΈΠ΅ ΠΊΠ°ΡΠ΅ΡΡΠ²Π° ΡΡΡΡΠΊΡΡΡΠ½ΠΎΠΉ ΠΈΠ΄Π΅Π½ΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ Π±ΠΈΠΎΠΌΠ΅Π΄ΠΈΡΠΈΠ½ΡΠΊΠΈΡ
ΡΠΈΠ³Π½Π°Π»ΠΎΠ² Ρ Π»ΠΎΠΊΠ°Π»ΡΠ½ΠΎ ΡΠΎΡΡΠ΅Π΄ΠΎΡΠΎΡΠ΅Π½Π½ΡΠΌΠΈ ΠΏΡΠΈΠ·Π½Π°ΠΊΠ°ΠΌΠΈ Π·Π° ΡΡΠ΅Ρ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠΊΠΈ Π½ΠΎΠ²ΡΡ
ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² ΡΠ΅ΡΠ΅Π½ΠΈΡ ΠΏΠΎΡΡΠ°Π²Π»Π΅Π½Π½ΠΎΠΉ Π·Π°Π΄Π°ΡΠΈ. Π Π°ΡΡΠΌΠΎΡΡΠ΅Π½Π° ΠΏΡΠΎΠ±Π»Π΅ΠΌΠ° ΠΏΡΠΎΠ΅ΠΊΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΈΠ½ΡΠ΅Π»Π»Π΅ΠΊΡΡΠ°Π»ΡΠ½ΡΡ
ΠΊΠΎΠΌΠΏΡΡΡΠ΅ΡΠ½ΡΡ
ΠΊΠ°ΡΠ΄ΠΈΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠΈΡΡΠ΅ΠΌ ΠΏΠΎΠ΄Π΄Π΅ΡΠΆΠΊΠΈ ΠΏΡΠΈΠ½ΡΡΠΈΡ ΡΠ΅ΡΠ΅Π½ΠΈΠΉ ΠΈ ΡΡΠΎΡΠΌΡΠ»ΠΈΡΠΎΠ²Π°Π½Ρ ΠΎΡΠ½ΠΎΠ²Π½ΡΠ΅ ΡΡΠ°ΠΏΡ ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠΈ Π±ΠΈΠΎΠΌΠ΅Π΄ΠΈΡΠΈΠ½ΡΠΊΠΈΡ
ΡΠΈΠ³Π½Π°Π»ΠΎΠ² Ρ Π»ΠΎΠΊΠ°Π»ΡΠ½ΠΎ ΡΠΎΡΡΠ΅Π΄ΠΎΡΠΎΡΠ΅Π½Π½ΡΠΌΠΈ ΠΏΡΠΈΠ·Π½Π°ΠΊΠ°ΠΌΠΈ. ΠΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½ ΠΎΠ±ΠΎΠ±ΡΠ΅Π½Π½ΡΠΉ ΠΌΠ΅ΡΠΎΠ΄ ΡΡΡΡΠΊΡΡΡΠ½ΠΎΠΉ ΠΈΠ΄Π΅Π½ΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ Π±ΠΈΠΎΠΌΠ΅Π΄ΠΈΡΠΈΠ½ΡΠΊΠΈΡ
ΡΠΈΠ³Π½Π°Π»ΠΎΠ² Ρ Π»ΠΎΠΊΠ°Π»ΡΠ½ΠΎ ΡΠΎΡΡΠ΅Π΄ΠΎΡΠΎΡΠ΅Π½Π½ΡΠΌΠΈ ΠΏΡΠΈΠ·Π½Π°ΠΊΠ°ΠΌΠΈ Ρ ΠΏΠΎΠΌΠΎΡΡΡ ΡΠΈΡΡΠΎΠ²ΠΎΠ³ΠΎ Π½Π΅Π»ΠΈΠ½Π΅ΠΉΠ½ΠΎΠ³ΠΎ ΡΠΈΠ»ΡΡΡΠ°. ΠΡΠΎΠ²Π΅Π΄Π΅Π½ΠΎ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠ΅ ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠΎΠ² Π½Π΅Π»ΠΈΠ½Π΅ΠΉΠ½ΠΎΠ³ΠΎ ΡΠΈΠ»ΡΡΡΠ° Π² Π·Π°Π΄Π°ΡΠ΅ ΡΡΡΡΠΊΡΡΡΠ½ΠΎΠΉ ΠΈΠ΄Π΅Π½ΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ Π±ΠΈΠΎΠΌΠ΅Π΄ΠΈΡΠΈΠ½ΡΠΊΠΈΡ
ΡΠΈΠ³Π½Π°Π»ΠΎΠ² Ρ Π»ΠΎΠΊΠ°Π»ΡΠ½ΠΎ ΡΠΎΡΡΠ΅Π΄ΠΎΡΠΎΡΠ΅Π½Π½ΡΠΌΠΈ ΠΏΡΠΈΠ·Π½Π°ΠΊΠ°ΠΌΠΈ, Π²ΡΠΏΠΎΠ»Π½Π΅Π½ ΡΠΈΠ½ΡΠ΅Π· ΠΊΡΠΈΡΠ΅ΡΠΈΡ ΠΊΠ°ΡΠ΅ΡΡΠ²Π° ΡΡΡΡΠΊΡΡΡΠ½ΠΎΠΉ ΠΈΠ΄Π΅Π½ΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ ΡΠΏΡΠΎΠ΅ΠΊΡΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠ³ΠΎ Π½Π΅Π»ΠΈΠ½Π΅ΠΉΠ½ΠΎΠ³ΠΎ ΡΠΈΠ»ΡΡΡΠ°, Π° ΡΠ°ΠΊΠΆΠ΅ Π²ΡΠΏΠΎΠ»Π½Π΅Π½Π° ΡΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΠ°Π»ΡΠ½Π°Ρ ΠΏΡΠΎΠ²Π΅ΡΠΊΠ° ΠΊΠ°ΡΠ΅ΡΡΠ²Π° ΡΡΡΡΠΊΡΡΡΠ½ΠΎΠΉ ΠΈΠ΄Π΅Π½ΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ ΠΏΡΠΈ Π·Π°Π΄Π°Π½ΠΈΠΈ ΡΠ°Π·Π»ΠΈΡΠ½ΡΡ
ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠΎΠ² Π½Π΅Π»ΠΈΠ½Π΅ΠΉΠ½ΠΎΠ³ΠΎ ΡΠΈΠ»ΡΡΡΠ°. Π‘Π΄Π΅Π»Π°Π½Ρ Π²ΡΠ²ΠΎΠ΄Ρ ΠΎΠ± ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΡΡΠΈ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΡ ΡΠ°Π·Π»ΠΈΡΠ½ΡΡ
ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ ΠΏΠΎΠ»Π΅Π·Π½ΠΎΠ³ΠΎ ΡΠΈΠ³Π½Π°Π»Π° Π΄Π»Ρ ΡΡΡΡΠΊΡΡΡΠ½ΠΎΠΉ ΠΈΠ΄Π΅Π½ΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ Π±ΠΈΠΎΠΌΠ΅Π΄ΠΈΡΠΈΠ½ΡΠΊΠΈΡ
ΡΠΈΠ³Π½Π°Π»ΠΎΠ² Ρ Π»ΠΎΠΊΠ°Π»ΡΠ½ΠΎ ΡΠΎΡΡΠ΅Π΄ΠΎΡΠΎΡΠ΅Π½Π½ΡΠΌΠΈ ΠΏΡΠΈΠ·Π½Π°ΠΊΠ°ΠΌΠΈ
The Association of Sarcopenia and Osteoporosis and Their Role in Falls and Fractures (Literature Review)
The progressive and generalized loss of skeletal muscle mass and strength leads to sarcopenia in elderly people. A new geriatric syndrome has been revealed β 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 β 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