Жарияланымдар / Публикации

Постоянный URI для этого сообществаhttps://cris.qyzpu.edu.kz/handle/123456789/2

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Автор: search.filters.author.Кassymbek OzhikenovТема: search.filters.subject.wearable ECG

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    Wearable ECG Device and Machine Learning for Heart Monitoring
    (Multidisciplinary Digital Publishing Institute, 2024-06-28)
    Zhadyra N. Alimbayeva
    ;
    Chingiz A. Alimbayev
    ;
    Кassymbek Ozhikenov
    ;
    Nurlan A. Bayanbay
    ;
    Aiman K. Ozhikenova
    With cardiovascular diseases (CVD) remaining a leading cause of mortality, wearable devices for monitoring cardiac activity have gained significant, renewed interest among the medical community. This paper introduces an innovative ECG monitoring system based on a single-lead ECG machine, enhanced using machine learning methods. The system only processes and analyzes ECG data, but it can also be used to predict potential heart disease at an early stage. The wearable device was built on the ADS1298 and a microcontroller STM32L151xD. A server module based on the architecture style of the REST API was designed to facilitate interaction with the web-based segment of the system. The module is responsible for receiving data in real time from the microcontroller and delivering this data to the web-based segment of the module. Algorithms for analyzing ECG signals have been developed, including band filter artifact removal, K-means clustering for signal segmentation, and PQRST analysis. Machine learning methods, such as isolation forests, have been employed for ECG anomaly detection. Moreover, a comparative analysis with various machine learning methods, including logistic regression, random forest, SVM, XGBoost, decision forest, and CNNs, was conducted to predict the incidence of cardiovascular diseases. Convoluted neural networks (CNN) showed an accuracy of 0.926, proving their high effectiveness for ECG data processing.
    Цитирования (OpenAlex) 55Цитирования (OpenCitations) 30
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    AN EARLY WARNING SYSTEM FOR HOSPITALS USING A WEARABLE ECG DEVICE AND MACHINE LEARNING
    (Mukhametzhan Tynyshbayev ALT University, 2024-09-26)
    Zhadyra N. Alimbayeva
    ;
    Chingiz A. Alimbayev
    ;
    Dilfuza Akhmedova
    ;
    Кassymbek Ozhikenov
    ;
    Zhansulu Ualieva
    According to the World Health Organization, many clinical errors in medical practice occur due to incomplete or untimely access to patient information. The introduction of nurse call systems significantly improves communication between medical staff and patients, increases response efficiency and patient satisfaction. Traditional nurse call systems based on patients' manual requests for medical care are insufficient to prevent unforeseen medical situations. This article proposes a new approach based on the wearable ECG devices developed by the authors for monitoring the cardiovascular system of patients. This approach uses machine learning techniques to analyze data from wearable ECG devices, which allows timely detection of dangerous patient conditions and necessary medical interventions. The proposed early warning system for hospitals performs various important functions, such as continuous recording of the electrical activity of the heart, preprocessing data to reduce noise and improve accuracy, extracting key parameters for further analysis, forming diagnostic indicators and a comprehensive assessment of the condition of the heart to identify potential anomalies.