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1UDC. 303.687.3
B RECEIVING AND RECORDING INFORMATION ABOUT THE STATUS AND PARAMETERS OF THE MEDICAL-BIOLOGICAL SYSTEM
Djumanov Jamoljon Khudoyqulovich Head of the Department, Compyuter Networks of the Tashkent University of Information Technologies named after Muhammad Al Khwarizmi, Uzbekistan
Rakhimov Baxtiyar Saidovich Head of theDepartment of Biophysics and Information technologies of Urgench
branch of Tashkent Medical Academy, Uzbekistan
Rakhimova Feroza Baxtiyarovna Teacher of the department of Biophysics and Information technologies of Urgench
branch of Tashkent Medical Academy, Uzbekistan [email protected]
Annotatsiya. Ushbu maqolada tibbiy-biologik tizimlarning holati va parametrlarini aniqlash, o'lchash va qayd qilish jarayoniga alohida e'tibor qaratilgan. Shuningdek, biosignallarni to'g'ri qayta ishlash va uzatish tibbiy diagnostikada muhim ahamiyatga ega bo'lib, elektrokardiogramma (EKG) signallarini tahlil qilish texnologiyalari va bu signallardagi asosiy qismlarni aniqlash masalalari chuqur tahlil qilingan. Xususan, EKG signallarining P, Q, R, S va T nuqtalari orqali yurakning elektr faoliyatini baholashdagi ahamiyati alohida ko'rib chiqilgan.Yurak kasalliklarini diagnostika qilishda muhim ahamiyatga ega bo'lgan QRS kompleksi va uni aniqlashdagi qiyinchiliklar tavsiflangan.
Maqolada shovqinlar, elektrodlardan kelib chiqadigan ta'sirlar va biosignallarning o'zgaruvchanligi kabi omillarni hisobga olgan holda, signallarni qayta ishlashning zamonaviy usullari o'rganilgan. Xususan, Wavelet Transformatsiyasi (WT) va Discrete Wavelet Transformatsiyasi (DWT) kabi usullar yurak patologiyalarini aniqlashda samarali ekanligi ta'kidlangan.Shuningdek, maqolada to'lqinli transformasiya texnologiyalari EKG signallarining har bir qismini aniqlash va yurak kasalliklarini aniqlashdagi yuqori aniqlikni ta'minlashda qanday rol o'ynashi tushuntirilgan. Turli xil filtrlar yordamida shovqinlarni filtrlash, EKG signallaridagi aniq ma'lumotlarni qayta ishlash va ularni tahlil qilish orqali yurakning normal va patologiyali holatlarini aniqlash imkoniyatlari ko'rib chiqilgan.
Maqolada O'zbekistonda Sog'liqni Saqlash sohasida axborot texnologiyalarining rivojlanishi va zamonaviy diagnostika usullarining joriy qilinishiga katta e'tibor qaratilganini ham yoritib beradi. Qolaversa, to'plangan ma'lumotlarni avtomatlashtirilgan tizimlar orqali qayta ishlash, yurak faoliyatidagi o'zgarishlarni monitoring qilish va diagnostika jarayonlarini yanada takomillashtirishga oid tadqiqotlarning natijalari keltirilgan. Yurak xastaliklari diagnostikasini takomillashtirish uchun raqamli qayta ishlash texnologiyalaridan foydalanish zarurligi maqola davomida yoritilgan.
Аннотация. В данной статье основное внимание уделяется процессу определения, измерения и регистрации состояния и параметров медико-биологических систем. Также в медицинской диагностике важна правильная обработка и передача биосигналов, глубоко анализируются технология анализа сигналов электрокардиограммы (ЭКГ) и вопросы выявления основных компонентов в этих сигналах. В частности, отдельно рассмотрено значение сигналов ЭКГ в оценке электрической активности сердца через точки P, Q, R, S и Т. Описан комплекс QRS, имеющий важное значение в диагностике заболеваний сердца, и трудности его определения.
В статье рассматриваются современные методы обработки сигналов с учетом таких
факторов, как шум, воздействие электродов и изменчивость биосигналов. В частности, отмечена эффективность таких методов, как вейвлет-преобразование (ВП) и дискретное вейвлет-преобразование (ДВП), при обнаружении патологий сердца. Также в статье объясняется, какую роль играют технологии вейвлет-преобразования в идентификации каждой части сигналов ЭКГ и обеспечении высокого качества. точность выявления заболеваний сердца. Рассмотрены возможности выявления нормальных и патологических состояний сердца путем фильтрации шумов с помощью различных фильтров, обработки и анализа специфической информации сигналов ЭКГ.
В статье освещено развитие информационных технологий и внедрение современных методов диагностики в сфере здравоохранения Узбекистана. Кроме того, приведены результаты исследований по обработке собранных данных посредством автоматизированных систем, мониторингу изменений сердечной деятельности и дальнейшему совершенствованию. представлены диагностические процессы. На протяжении всей статьи подчеркивается необходимость использования технологий цифровой обработки для улучшения диагностики заболеваний сердца.
Abstract. This article focuses on the process of determining, measuring and recording the status and parameters of medical-biological systems. Also, the correct processing and transmission of biosignals is important in medical diagnostics, and the technology of analyzing electrocardiogram (ECG) signals and the issues of identifying the main components in these signals are deeply analyzed. In particular, the importance of ECG signals in evaluating the electrical activity of the heart through P, Q, R, S, and T points is considered. The QRS complex, which is important in the diagnosis of heart diseases, and the difficulties in its determination are described.
The article examines modern methods of signal processing, taking into account such factors as noise, effects caused by electrodes and variability of biosignals. In particular, methods such as Wavelet Transform (WT) and Discrete Wavelet Transform (DWT) are noted to be effective in detecting heart pathologies. Also, the article explains how wavelet transform technologies play a role in identifying each part of ECG signals and providing high accuracy in detecting heart diseases. Possibilities of detecting normal and pathological conditions of the heart by filtering noises using different filters, processing and analyzing specific information from ECG signals were considered.
The article highlights the development of information technologies and the introduction of modern diagnostic methods in the field of healthcare in Uzbekistan. In addition, the results of research on the processing of collected data through automated systems, monitoring of changes in heart activity, and further improvement of diagnostic processes are presented. The need to use digital processing technologies to improve the diagnosis of heart diseases is highlighted throughout the article.
Kalit so'zlar: tibbiy-biologik tizim, biosignal, elektrodlar, elektr potensiallari, elektrokardiogramma, QRS kompleksi, shovqinlar, o'tkazuvchan filtrlar, Wavelet transformatsiyasi.
Ключевые слова: медико-биологическая система, биосигнал, электроды, электрические потенциалы, электрокардиограмма, комплекс QRS, шумы, кондуктивные фильтры, волновое преобразование.
Key words: medical-biological system, biosignal, electrodes, electric potentials, electrocardiogram, QRS complex, noises, conductive filters, Wavelet transformation. Introduction
Any medical-biological research is related to obtaining the necessary information and recording it. For this reason, various modern digital processing methods, models, algorithms, and software tools have been developed for obtaining and recording biosignals in the human body.
To obtain and record relevant information about the state and parameters of the medical-biological system, a set of structures is necessary. The main element is a measurement tool that directly interacts with the system itself—a sensitive element. Other elements are separated from the medical biological system, and sometimes parts of the measurement system are placed at a certain distance from the measured object.
Medical electronics directly transmit biosignals or process such signals under the influence of the biological system. Thus, the structure of receiving biosignals transforms medical, biological, and physiological information into an electronic signal. As the final element in the chain of signal acquisition, measurement, and digital processing, a measuring instrument is obtained, reflecting the information about the biological system in a convenient and understandable form for the observer. In most cases, elements that amplify the initial signal and transmit it over a distance are placed between the receiving structure and the measuring instrument. Medical-biological information is obtained through electrodes and sensors in medical electronics. Literature Review
As a result of the development of information technologies in the Republic of Uzbekistan, great attention is being paid to providing a wide range of information services, creating convenient software tools, and increasing the efficiency of using software products in the healthcare system. According to the World Health Organization, one of the effective ways to prevent death from heart attack is timely diagnosis based on digital processing algorithms of modern electrocardiographic (ECG) signals. Therefore, along with developed countries, scientific research on processing of ECG signals is being carried out in our country.
The main scientific works of the scientists and researchers of our republic V.K. Kabulov, B.N. Khidirov, MM. Musaev, A. Abduqayumov, Kh.N. Zaynidinov, S. Sayidaliev, J.Kh. Djumanov, O'.R. Hamdamov, F.F. Rajabov, R. Nasimov and others who have contributed to research in the field of digital processing of biomedical signals and diagnosis based on the detected results.
Research Methodology
Electrodes are conductors of a special shape that connect the measuring circuit with the biological system. In the medical field, electrodes are used for electromagnetic stimulation and treatment.
Figure 1.
The generation and propagation of excitation in the heart is studied not only by recording the difference in electrical potentials from some muscle cells or the surface of the heart, but also by recording the electrical changes that occur on the surface of the body due to the beating of the heart. As a result, changes in biopotentials are detected using electrodes placed on certain points of the body, providing an opportunity to record curves (Figure 1).
An electrocardiogram (ECG) records the electrical activity in the cardiac area of the heart. Electrical activity appears in the form of small potentials generated by the heart tissue, which are picked up by the electrodes of the ECG leads. The ECG signal contains important information about cardiac pathologies present in the heart and shows peaks called fiducial points represented by the five letters P, Q, R, S, and T
P QRS T
Wm Cvittiiex Wave
PR Qr
Interval Interval
Figure 2. Electrocardiogram (ECG) Signal Morhology
When analyzing the results of the recorded electrocardiogram, the important information lies in the sections of the curve called cardiac complexes, such as P-, Q-, R-, S-, and T-waves. During information acquisition from the curve, it is necessary to select and classify the specified "informative" sections according to certain criteria, as well as to build classes of selected sections through a description that represents the curve as a whole. In such a description, the names (indices, numbers) of section classes act as "letters," and the sequence of these indices can be understood as the "characters" of a specific language. This language helps analyze individual curves and, based on that, the entire set of curves describing the behavior of the studied object. This information-gathering process reflects the general approach to the study of graphical lines, as developed through experimental practices by practitioners analyzing curves visually.
ECG analysis and classification require accurate identification of waveform fiducial points from pre-recorded or real-time ECG signals as the primary input. In both cases, the acquisition of ECG data involves connecting sensors and conductors to the body. During this process, noise is also captured along with the original signal, significantly affecting the quality and classification of the ECG.
The QRS complex is the main decisive part of the ECG signal and reflects the ventricular contraction activity of the heart. Any QRS detector should be able to detect different QRS patterns, which can effectively help classify the ECG signal and detect various types of arrhythmias. Its appearance is crucial for automated detection of various features and forms the basis for several classification methods. First, the QRS complex must be identified for all automated ECG analysis algorithms. However, considering the physiological variability of the QRS complex and the presence of noise in the ECG signal, accurately calculating the QRS presents several challenges. In recent years, control machines for digital processing and calculation of many analog signals have been created to determine the QRS complex, ST-segment, R-wave, and other reliable points (Figure 3).
Micro SD
Figure 3.
This unit's function is to deliver and apply a clean signal by minimizing distortions in the ECG signal using a non-inverting active amplifier based on low-pass and high-pass filters and band-pass filters. Also, the combination of two filters (with a cut-off frequency of 0.5 Hz for low frequencies and 50 Hz for high frequencies) allows the removal of unwanted frequencies from the components of the ECG signal.
The QRS complex of the ECG signal represents the depolarization of the right and left ventricles of the heart and is considered the reference point for signal analysis. The P wave results from atrial depolarization, while the ventricles cause the remaining peaks. Signal diagnosis also relies on the morphology of the waves, including the duration of each peak and its intervals and segments (P wave, QRS complex, T wave, PR and QT intervals, PR and ST segments). However, the variable physiology of detection reference points between ECG sections, caused mainly by cardiac abnormalities, changes in the isoelectric line, or noise added to the ECG signal, complicates discrimination. Therefore, identifying each section in the ECG signal is crucial for improving the diagnosis of heart diseases, making it important for screening, diagnosis, and monitoring. Analysis and Results
The analog-to-digital conversion of ECG signals can introduce noise that affects the quality of various signal data. Noise causes can vary: mains noise (50 or 60 Hz frequency), baseline deviation, or muscle electrical activity (EMG). These processes are considered in a phase-based ECG signal analysis model. Initially, different sources of ECG data are examined, such as clinically pre-recorded and real-time ECG sensing data. Various techniques for removing noise during ECG acquisition are then discussed. The next step is to identify the reliable points of the ECG signal, which is critical for accurately classifying different heart diseases. Each wave and segment of the ECG signal is significant in determining the type of arrhythmia. In the final step, after properly selecting the data source and identifying reliable ECG points, the ECG signal can be analyzed using traditional signal processing or machine learning to detect and classify various heart diseases.
One method for detecting different points in the ECG signal is the Wavelet Transform (WT) method. The wavelet transform decomposes and separates the signal into components, allowing simultaneous observation of the signal's time and frequency information. This makes it possible to study a certain part of the signal, such as the QRS complex in the ECG, which provides event-related information. By knowing its time interval, reliable points can be identified and features of the ECG can be extracted.
The Wavelet Transform (WT) was introduced to overcome some of the shortcomings of and serve as an alternative to the Short-Time Fourier Transform (STFT). Discrete Wavelet Transform Qurilish va Ta 'lim ilmiy jurnali 3-jild, 4-son https://jurnal.qurilishtalim.uz
(DWT) is easier to use for signal analysis and synthesis, with computational efficiency and reduced computation time. DWT represents a signal in both the time and frequency domains. This transformation has become an important tool for analyzing biomedical signals like ECG. DWT converts the ECG signal into different levels of resolution by decomposing the signal. This scaled signal can then be analyzed using different filters to obtain various points. Conclusions
This article emphasizes the importance of measuring and recording the status and parameters of medical-biological systems. It studies in detail the possibilities for processing and transmitting biosignals using electronic devices, particularly focusing on the analysis of electrocardiogram (ECG) signals. The identification of each component of the ECG signal is crucial for improving heart disease diagnosis, screening, and monitoring. The importance of using digital signal processing methods, including Wavelet Transform (WT) and Discrete Wavelet Transform (DWT), to identify and classify reliable points for detecting heart diseases is emphasized. Thus, this article presents current problems and their solutions in obtaining and processing medical and biological information.
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