Wearable ECG Device and Machine Learning for Heart Monitoring

Alimbayeva Z. Alimbayev C. Ozhikenov K. Bayanbay N. Ozhikenova A.
July 2024 Multidisciplinary Digital Publishing Institute (MDPI)

Sensors
2024 #24 Issue 13

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.

ECG database , ECG monitoring system , ECG signal , machine learning , PQRST wave detection , wearable ECG device

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Department of Robotics and Technical Means of Automation, Satbayev University, Almaty, 050013, Kazakhstan
Department of Information Technologies and Library Affairs, Kazakh National Women’s Teacher Training University, Almaty, 050000, Kazakhstan
Joldasbekov Institute of Mechanics and Engineering, Almaty, 050010, Kazakhstan

Department of Robotics and Technical Means of Automation
Department of Information Technologies and Library Affairs
Joldasbekov Institute of Mechanics and Engineering

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