kaz44Вестник Университета Шакарима. Серия технические наукиBulletin of Shakarim University. Technical Sciences2788-79953006-0524«Шәкәрім университеті» КеАҚ10.53360/2788-7995-2025-4(20)-1kaz44-2149Research ArticleАВТОМАТИЗАЦИЯ И ИНФОРМАЦИОННЫЕ ТЕХНОЛОГИИ (ОРИГИНАЛЬНАЯ СТАТЬЯ)AUTOMATION AND INFORMATION TECHNOLOGY (ORIGINAL ARTICLE)АВТОМАТИЗИРОВАННАЯ КЛАССИФИКАЦИЯ ГЕМОДИНАМИЧЕСКИ ЗНАЧИМЫХ АРИТМИЙ НА ОСНОВЕ ЭКГ-ПРИЗНАКОВAUTOMATED CLASSIFICATION OF HEMODYNAMICALLY SIGNIFICANT ARRHYTHMIAS BASED ON ECG FEATUREShttps://orcid.org/0000-0002-7963-7439БекбайА. Т.BekbayA. T.

Айнұр Тоқтарғалиқызы Бекбай – постдокторант, старший преподаватель кафедры «Робототехники и технических средств автоматики»

050013, Республика Казахстан. г. Алматы. ул. Сатпаева, 22

Ainur Bekbay – postdoctoral researcher, Senior Lecturer of the Department of Robotics and Engineering Tools of Automation 

050013, Republic of Kazakhstan, Almaty 22 Satbayev Street 

a.bekbay@satbayev.universityhttps://orcid.org/0000-0002-7440-5591БигалиеваЖ. С.BigaliyevaZh. S.

Жанар Серикхановна Бигалиева – старший преподаватель кафедры «Робототехники и технических средств автоматики»

050013, Республика Казахстан. г. Алматы. ул. Сатпаева, 22

Zhanar Bigaliyeva – Senior Lecturer of the Department of Robotics and Engineering Tools of Automation

050013, Republic of Kazakhstan, Almaty 22 Satbayev Street

zh.bigaliyeva@satbayev.universityhttps://orcid.org/0009-0007-1717-5041БайтургановаВ. К.BaiturganovaV. K.

Винера Канапияевна Байтурганова – старший преподаватель кафедры «Робототехники и технических средств автоматики»

050013, Республика Казахстан. г. Алматы. ул. Сатпаева, 22

Vinera Baiturganova – Senior Lecturer of the Department of Robotics and Engineering Tools of Automation

050013, Republic of Kazakhstan, Almaty 22 Satbayev Street

v.baiturganova@satbayev.universityhttps://orcid.org/0000-0002-2628-2515АлимбаеваЖ. Н.AlimbayevaZh. N.

Жадыра Нурдаулетовна Алимбаева – доктор PhD, преподаватель кафедры «Информационные технологии и библиотечное дело космической техники и технологий»

050000, Республика Казахстан, г. Алматы, ул. Гоголя, 114

Zhadyra Alimbayeva – PhD, Lecturer of the Department of Information Technologies and Library Science in Space Engineering and Technology

050000, Republic of Kazakhstan, Almaty, 114 Gogol Street

alimbayeva.zhadyra@qyzpu.edu.kzhttps://orcid.org/0000-0003-2900-8025ИсабековЖ. Н.IssabekovZh. N.

Жанибек Назарбекулы Исабеков – PhD, ассоциированный профессор кафедры «Робототехники и технических средств автоматики»

050013, Республика Казахстан. г. Алматы. ул. Сатпаева, 22

Zhanibek Issabekov – PhD, Associate Professor of the Department of Robotics and Engineering Tools of Automation

050013, Republic of Kazakhstan, Almaty 22 Satbayev Street

z.issabekov@satbayev.universitySatbayev UniversityКазахстанSatbayev UniversityKazakhstanКазахский национальный женский педагогический университетКазахстанKazakh National Women's Teacher Training UniversityKazakhstan20252501202614(20)613Copyright © Бекбай А.Т., Бигалиева Ж.С., Байтурганова В.К., Алимбаева Ж.Н., Исабеков Ж.Н., 20262026Бекбай А.Т., Бигалиева Ж.С., Байтурганова В.К., Алимбаева Ж.Н., Исабеков Ж.Н.Bekbay A.T., Bigaliyeva Z.S., Baiturganova V.K., Alimbayeva Z.N., Issabekov Z.N.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://tech.vestnik.shakarim.kz/jour/article/view/2149

В статье представлен метод классификации гемодинамически значимых аритмий (ГЗА) на основе параметров электрокардиограммы (ЭКГ) без использования дополнительных визуализирующих диагностических методов. Были выделены ключевые признаки, такие как длительность комплекса QRS, интервалы RR и частота сердечных сокращений (ЧСС). Классификация аритмий выполнена на основе данных базы MIT-BIH Arrhythmia Database. Разработаны визуализации и логическая схема для автоматического определения ГЗА. Цель исследования – разработка и валидация алгоритма классификации гемодинамически значимых аритмий, основанного исключительно на электрокардиографических признаках. Предлагаемая методика опирается на ранее опубликованные исследования, посвящённые использованию ЭКГпризнаков в диагностике аритмий, и направлена на повышение доступности диагностики в условиях ограниченных ресурсов. Основная цель работы заключается в расширении доступности диагностики сердечных аритмий посредством интерпретируемых инженерных решений.

This paper presents an automated method for the classification of hemodynamically significant arrhythmias (HSA) based solely on electrocardiographic (ECG) features, without the use of additional imaging diagnostic techniques. The proposed approach relies on key ECG parameters, including QRS complex duration, RR intervals, and heart rate (HR). The study is based on data from the open-access MIT-BIH Arrhythmia Database, which contains multiple types of cardiac rhythm disturbances. A comparative analysis of arrhythmia classes was conducted, leading to the identification of diagnostically significant predictors associated with hemodynamic instability. Logical decision rules and a decision tree model were developed to enable automatic recognition of HSA and clinical risk stratification. The proposed algorithm demonstrates high interpretability and practical applicability for real-time monitoring systems. The results confirm that ECG-based features alone can be effectively used for preliminary detection of dangerous arrhythmias. The developed approach is especially valuable for telemedicine systems and healthcare facilities with limited access to expensive diagnostic equipment.

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The authors declare that there are no conflicts of interest present.