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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.
Цитирования (OpenAlex) 2Цитирования (OpenCitations) 1
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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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Prediction of Percutaneous Coronary Intervention from Clinical and ECG Data Using Machine Learning: A Retrospective Single-Center Observational Study
(Multidisciplinary Digital Publishing Institute, 2026-05-06)
Zhadyra N. Alimbayeva
;
Chingiz A. Alimbayev
;
Kassymbek Ozhikenov
;
Kairat Karibayev
;
Aiman K. Ozhikenova
The aim of this study was to evaluate the feasibility of predicting percutaneous coronary intervention (PCI) based on clinical, laboratory, and electrocardiographic data available at various stages of hospitalization. A retrospective single-center study was conducted, including 137 patients with suspected coronary artery disease. The fact that PCI was performed during the current hospitalization was considered as the endpoint. Taking into account the temporary availability of data, three sets of signs were formed: basic (SAFE), including indicators available at admission; clinical (CLINICAL); and extended (EXTENDED), supplemented with glycemic parameters. Logistic regression, random forest, and gradient boosting were used to build the models. The assessment was carried out using repeated stratified cross-validation (5 × 10). The main metrics were ROC-AUC, PR-AUC, accuracy and F1-measure. The models demonstrated moderate predictive ability. The basic model (SAFE) showed a ROC-AUC of 0.734 ± 0.092, while the best results were achieved using an extended model based on a random forest (ROC-AUC 0.755 ± 0.079). The addition of glycemic parameters provided a moderate improvement in prediction quality. In the logistic regression, the most significant predictor was the presence of type 2 diabetes mellitus (OR = 7.36; p < 0.001). The results indicate the potential for using non-invasive data to assess the likelihood of PCI in the early stages of hospitalization. However, the models show moderate accuracy and require further validation on larger and more independent samples.
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A Structured Critical Review of Machine Learning Approaches for ECG-Based Detection of Dysglycemia and Their Translational Readiness
(Multidisciplinary Digital Publishing Institute, 2026-05-27)
Zhadyra N. Alimbayeva
;
Chingiz A. Alimbayev
;
Kassymbek Ozhikenov
;
Aiman K. Ozhikenova
;
Ussen Shylmyrza
This structured critical review provides a comparative and analytically grounded overview of machine learning (ML) approaches for electrocardiography (ECG)-based detection of dysglycemia, with a specific focus on translational readiness for clinical screening. A structured literature search across PubMed, Scopus, Web of Science, and IEEE Xplore identified 183 records, of which 17 studies were included following predefined screening criteria and PRISMA-guided selection principles. The included studies demonstrate substantial heterogeneity in dataset size (ranging from 25,000 subjects), ECG acquisition modalities (single-lead, 12-lead, wearable), feature representations (raw signals, heart rate variability, engineered features), and ML strategies (classical algorithms, deep learning, and multimodal models). Reported model performance is generally high, with accuracy values frequently exceeding 0.85 and area under the curve (AUC) ranging from 0.78 to 0.99. Smaller experimental studies often report inflated performance (up to 96–99% accuracy), whereas large-scale population-based investigations demonstrate more moderate but clinically plausible results (AUC ≈ 0.80–0.85). External validation, a key requirement for clinical applicability, was performed in only a limited subset of studies (approximately 12%). From a physiological perspective, ML models exploit ECG alterations associated with dysglycemia, including reduced heart rate variability, QT interval prolongation, and changes in ventricular depolarization and repolarization dynamics. However, the relationship between metabolic dysfunction and ECG signals remains indirect. A key finding of this review is the mismatch between reported predictive performance and translational readiness. The majority of studies (≈65–70%) are classified as early-stage (Level 1–2 or 2–3), relying on small, single-center datasets and internal validation. Only a minority of studies achieve near-translational maturity (Level 4), characterized by large-scale datasets and external validation. ECG-based dysglycemia detection represents a promising non-invasive and scalable screening paradigm. However, its clinical translation is constrained by the lack of standardized ECG acquisition protocols, limited dataset diversity, insufficient external validation, and fragmented methodological approaches. Future research should prioritize large multi-center datasets, standardized feature extraction pipelines, hybrid interpretable models, and prospective validation to enable robust, generalizable, and clinically deployable screening systems.
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Development and Clinical Evaluation of a Wearable 12-Lead Electrocardiographic Platform with Automated ECG Analysis for Telemedicine Applications
(Multidisciplinary Digital Publishing Institute, 2026-08-30)
Zhadyra N. Alimbayeva
;
Chingiz A. Alimbayev
;
Kassymbek Ozhikenov
;
Kairat Karibayev
;
Aiman K. Ozhikenova
Wearable electrocardiographic technologies have become increasingly important for continuous cardiac monitoring; however, most existing portable systems are limited by the number of recorded leads or provide only basic signal acquisition without advanced automated analysis. This study presents a third-generation wearable twelve-lead electrocardiographic platform developed for multilead ECG acquisition and automated spatial ECG analysis. Compared with the previous generation, the hardware modification primarily consists of architectural consolidation: functions previously distributed across an STM32 microcontroller and separate wireless communication modules are integrated into a single ESP32-S3-based architecture, while the ECG acquisition principle, ten-electrode configuration, and sampling rate remain unchanged. The main methodological contribution of the present work is the software pipeline for lead-specific ST80 measurement and analysis of ST-segment deviations across anatomically contiguous leads. The system uses an ADS1298 analog front-end for synchronized multichannel ECG acquisition. The host software performs digital preprocessing, R-peak detection, ECG feature extraction, twelve-lead reconstruction, lead-specific ST80 measurement, contiguous-lead analysis, and generation of a preliminary computer-assisted ECG report. The developed platform was clinically evaluated using sequential recordings acquired with the proposed system and a reference clinical electrocardiograph. Quantitative comparison of automated PR, QRS, QT, and QTc measurements in 30 paired recordings demonstrated positive correlations with the reference BTL Flexi 12 ECG (r = 0.756–0.820, all p < 0.001), with mean absolute errors ranging from 2.53 ms for QRS duration to 10.40 ms for the QT interval. The system successfully recorded diagnostically interpretable twelve-lead ECGs in all participants and produced stable signal quality suitable for clinical assessment. The software automatically identified ECG waves and intervals, reconstructed twelve-lead recordings, evaluated ST-segment deviations across individual leads, and localized ischemia-related changes according to standard anatomical lead groups. Integration of signal acquisition, processing, visualization, and automated interpretation into a single telemedicine-oriented platform reduced hardware complexity while maintaining reliable multichannel ECG monitoring. The proposed wearable platform demonstrates the feasibility of combining compact embedded hardware with automated multilead ECG analysis for remote cardiovascular monitoring. The presented architecture provides a practical foundation for telemedicine applications and may support earlier recognition of clinically significant electrocardiographic abnormalities during ambulatory monitoring.

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