Article
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Preserved in Portico This version is not peer-reviewed
Wearable Ecg Device and Machine Learning for Heart Monitoring
Version 1
: Received: 22 May 2024 / Approved: 23 May 2024 / Online: 23 May 2024 (12:38:45 CEST)
A peer-reviewed article of this Preprint also exists.
Alimbayeva, Z.; Alimbayev, C.; Ozhikenov, K.; Bayanbay, N.; Ozhikenova, A. Wearable ECG Device and Machine Learning for Heart Monitoring. Sensors 2024, 24, 4201. Alimbayeva, Z.; Alimbayev, C.; Ozhikenov, K.; Bayanbay, N.; Ozhikenova, A. Wearable ECG Device and Machine Learning for Heart Monitoring. Sensors 2024, 24, 4201.
Abstract
As cardiovascular diseases continue to be a leading cause of mortality, recent-ly, wearable devices for monitoring cardiac activity have gained much interest among medical community. This paper introduces an innovative ECG monitoring system based on a single – lead ECG machine enhanced with machine learning methods. The system only processes and analyses the ECG data, but also 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 REST API architecture style was designed to fa-cilitate interaction with web-based segment of the system. The module is responsible for receiving data in real time from microcontroller and their deliver to web – based segment. Algorithms for analyzing ECG signals have been developed, including band filter artifact removal, K-means clustering for signal segmentation, and PQRST analysis. Machine leaning methods as Isolation Forest have been employed for ECG anomaly detection. Moreover, a comparative analysis with various machine learning methods, including lo-gistic regression, random forest, SVM, XGBoost, decision forest and, CNNs was conducted to predict cardiovascular diseases. Convoluted Neural Networks (CNN) showed an accu-racy of 0.926, proving high effectiveness in ECG data process.
Keywords
wearable ECG device; ECG monitoring system; ECG database; ECG signal; PQRST wave detection; machine learning
Subject
Engineering, Other
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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