Malokhat T. Magrupova, PhD student, senior lecturer at the Department of Metrology, Standardization and Certification in the named university. The author of more than 20 scientific publications. Area expertise: metrological assurance of the reliability of medical and biological objects.
Malokhat T. Magrupova, PhD student, senior lecturer at the Department of Metrology, Standardization and Certification in the named university. The author of more than 20 scientific publications. Area expertise: metrological assurance of the reliability of medical and biological objects.
Yokubjon T. Talatov, Master in Biomedical Engineering (2015), PhD student in Biomedical Engineering
The author of more than 15 scientific publications. Area expertise: algorithmic and software analysis and processing ‒ biomedical information.
Yokubjon T. Talatov, Master in Biomedical Engineering (2015), PhD student in Biomedical Engineering
The author of more than 15 scientific publications. Area expertise: algorithmic and software analysis and processing ‒ biomedical information.
Introduction. Cardiovascular disease occupies an important place throughout the world, which necessitates the development of more effective modern means of diagnosis and treatment. The primary diagnosis of heart disease is based on analysis and processing of an electrocardiogram (ECG). Despite the fact that there are many methods and algorithms for ECG analysis and processing, one of the urgent problems of cardiology remains to obtain the most complete information about heart electric potential, respectively, the behavior of the waves P, Q, R, S and T.
Aim. Development of algorithms and software for processing and analysis of electrocardiograms (ECGs), as well as calculation of heart rate and detection of arrhythmias based on Labview.
Materials and methods. The methods for removing noise using the wavelet transform method to eliminate baseline deviation ,to extract ECG signs ,to calculate heart rate and to detect arrhythmias based on Labview have been adopted as a mathematical apparatus for processing and analyzing ECGs.
Results. Organizing of the ECG database, developing algorithms for converting the ECG file of the database into a useful format for Labview, processing of the ECG signal with removing noise from the original ECG signal, extracting signs for obtaining ECG diagnostic indicators, calculating heart rate and detecting arrhythmias.
Conclusion. An analysis of the results demonstrates that systematic approaches to evaluating ECG signals allow to avoid one-way decisions and to integrate different methods into an integrated system of ideas of the state. The implementation of the proposed algorithms using Labview programming system ensures the removal of noise and artifacts, the extraction of the necessary ECG signs, the calculation of heart contractions and the detection of arrhythmias.
Introduction. Cardiovascular disease occupies an important place throughout the world, which necessitates the development of more effective modern means of diagnosis and treatment. The primary diagnosis of heart disease is based on analysis and processing of an electrocardiogram (ECG). Despite the fact that there are many methods and algorithms for ECG analysis and processing, one of the urgent problems of cardiology remains to obtain the most complete information about heart electric potential, respectively, the behavior of the waves P, Q, R, S and T.
Aim. Development of algorithms and software for processing and analysis of electrocardiograms (ECGs), as well as calculation of heart rate and detection of arrhythmias based on Labview.
Materials and methods. The methods for removing noise using the wavelet transform method to eliminate baseline deviation ,to extract ECG signs ,to calculate heart rate and to detect arrhythmias based on Labview have been adopted as a mathematical apparatus for processing and analyzing ECGs.
Results. Organizing of the ECG database, developing algorithms for converting the ECG file of the database into a useful format for Labview, processing of the ECG signal with removing noise from the original ECG signal, extracting signs for obtaining ECG diagnostic indicators, calculating heart rate and detecting arrhythmias.
Conclusion. An analysis of the results demonstrates that systematic approaches to evaluating ECG signals allow to avoid one-way decisions and to integrate different methods into an integrated system of ideas of the state. The implementation of the proposed algorithms using Labview programming system ensures the removal of noise and artifacts, the extraction of the necessary ECG signs, the calculation of heart contractions and the detection of arrhythmias.
The authors declare that there are no conflicts of interest present.