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Using The Machine Learning Method in Portable ECG Monitoring System to Identify Diagnostically Complex Heart Diseases
(2023-06-29)
Aidana K. Yerkebay
;
Zhadyra N. Alimbayeva
;
Chingiz A. Alimbayev
;
Aiman K. Ozhikenova
;
Nurlan A. Bayanbay
Due to the high level of disability and death caused by heart diseases worldwide, the problem of combating cardiovascular diseases is becoming more and more important and relevant. In fact, one of the leading causes of death worldwide has long been cardiovascular disease, according to the World Health Organization. Among heart diseases, the most dangerous to life is myocardial infarction. In connection with the rapid morbidity, early diagnosis of this disease is one of the topical problems of modern public health care. At present, although there is a wide range of different statistical, math methods and computer tools for the processing of cardiological information, the task of increasing the accuracy of methods for the analysis of electrocardiograms and diagnostic tools in this field of study remained relevant. Taking into account the imperfect tools and systems for diagnosing heart diseases, as well as the human factor, the lack of a method that can help doctors to diagnose myocardial infarction early requires the development of new diagnostic information methods. This article presents a neural network analysis methodology for diagnosing myocardial infarction based on the joint analysis of direct and reverse signs of myocardial infarction from electrocardiographic information obtained from a portable cardiac analyzer.
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Development of an atrioventricular block prediction of method for portable heart monitoring system
(Kharkiv State Academy of Physical Culture, 2022-06-30)
Ainur Bekbay
;
Zhadyra N. Alimbayeva
;
Chingiz A. Alimbayev
;
Nurlan A. Bayanbay
;
Кassymbek Ozhikenov
The object of the study is a portable system that allows real-time monitoring of the state of the heart for the timely provision of medical care. The task of detecting atrioventricular (AV) blocks in the conditions of free motor activity of the patient is being solved. To develop a method for detecting AV block, models of the electrical activity of the heart were used to take into account the spatiotemporal organization of the process of spreading excitation, analyze the dynamics of the behavior of the cardiovascular systems (CVS) for any value of the period of atrial excitation, and assess the degree of fitness of the CVS. The proposed method made it possible to determine the heart rate (HR) at which the development of AV block is possible. AV block of the III degree – heart rate 304 bpm; AV block of the II degree with the loss of half of the impulses – heart rate 260 bpm; AV block II degree with loss of individual impulses – heart rate 234 bpm; AV block of the 1st degree – heart rate 200 bpm. Prediction of AV block allows assessing the degree of “training” of the patient’s heart. The obtained quantitative results are consistent with the heart rate values known to modern health care. The developed method was implemented on the basis of a portable ECG monitoring system previously developed by the authors. Tests of the portable ECG monitoring system indicate an increase in the sensitivity and specificity of diagnosing cardiac arrhythmia and confirm the achievement of the goal of this study: improving the efficiency of diagnostics and expanding the functionality of the portable ECG monitoring system
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Қазақ ұлттық қыздар педагогикалық университеті / Казахский национальный женский педагогический университет

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