Қазақ ұлттық қыздар педагогикалық университеті / Казахский национальный женский педагогический университет

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Material Database for Automated Material Selection in Surgical Robotic Manipulators
(European Organization for Nuclear Research, 2026-07-22)
Zhadyra N. Alimbayeva
This dataset contains the component-oriented material database developed for the study "Development of a Material Database and an Automated Selection Algorithm for a Surgical Robotic Manipulator". The database includes engineering materials, their mechanical, biomedical, environmental, manufacturing, and economic properties, as well as the candidate material assignments used for the numerical case study and TOPSIS-based material selection.
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ЦИФРОВОЙ ЭСТЕЗИОМЕТР ДЛЯ ОЦЕНКИ ЧУВСТВИТЕЛЬНОСТИ ПОЛОСТИ РТА
(Mukhametzhan Tynyshbayev ALT University, 2025-06-03)
Akzhol Nurdanali
;
Chingiz A. Alimbayev
;
Кassymbek Ozhikenov
;
Zhadyra N. Alimbayeva
;
Сана Абылгазинова
Съемные зубные протезы играют важную роль в восстановлении как функции, так и эстетики у пациентов с потерей зубов. Обеспечение надлежащего соответствия между протезом, анатомией полости рта и чувствительностью слизистой оболочки имеет решающее значение для комфорта пациента и эффективности протеза. Частой причиной дискомфорта и последующего отторжения этих протезов является боль, испытываемая под ними. Для решения этой проблемы в процессе изготовления применяется мягкая подкладка из эластичного материала, которая равномерно распределяет давление, оказываемое протезом. Правильный выбор толщины подкладки имеет важное значение, поскольку она напрямую влияет на комфорт и эффективность протеза. В настоящее время этот выбор во многом зависит от клинического опыта стоматолога и субъективной обратной связи пациента. В этом исследовании мы представляем новое устройство, предназначенное для объективной оценки чувствительности полости рта, что облегчает оптимизацию толщины мягкой подкладки в съемных зубных протезах. Были разработаны два лабораторных прототипа с использованием микроконтроллеров STM32f103c8t6 и Arduino Nano, каждый из которых включал различные методики измерения.
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Comparative Analysis of Deep Learning Architectures for Osteoporosis Detection in Knee Radiographs: From Custom CNN to Transfer Learning
(2026-01-01)
Assiya Boltaboyeva
;
Zhanel Baigarayeva
;
Баглан Иманбек
;
Кassymbek Ozhikenov
;
Zhadyra N. Alimbayeva
Osteoporosis is a systemic skeletal disorder affecting millions worldwide, characterized by decreased bone mineral density and increased fracture risk. Early and accurate detection from knee radiographs remains clinically challenging due to the subtlety of trabecular changes and limited availability of annotated datasets. This study presents a comparative analysis of three deep learning architectures — a custom Convolutional Neural Network (CNN), ResNet50 with partial fine-tuning, and EfficientNet-B3 with full fine-tuning — for binary classification of knee X-ray images into Normal and Osteoporosis categories. Methods. The Osteoporosis Knee X-ray Dataset (372 images) was used as the benchmark. All models were trained with Contrast-Limited Adaptive Histogram Equalization (CLAHE)-based contrast enhancement, geometric and occlusion augmentations, and a class-weighted cross-entropy loss function to address class imbalance. Early stopping and L2 regularization were applied to improve generalization. Results. Experimental results demonstrate that EfficientNet-B3 achieved the highest overall performance with an accuracy of 80.00%, recall of 89.74%, and F1-score of 82.35%, making it the most suitable model for clinical screening where sensitivity is prioritized. Custom CNN attained the highest Receiver Operating Characteristic – Area Under the Curve of 87.75%, indicating strong discriminative capacity despite its compact architecture. Conclusions. ResNet50 with partial fine-tuning showed competitive precision (76.32%) and Area Under the Curve (86.89%). These findings provide insights into the trade-offs between model complexity, transfer learning strategy, and diagnostic performance for small-scale medical imaging tasks.
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AUTOMATED CLASSIFICATION OF HEMODYNAMICALLY SIGNIFICANT ARRHYTHMIAS BASED ON ECG FEATURES
(Shakarim University, 2026-01-25)
Ainur Bekbay
;
Zh. S. Bigaliyeva
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Vinera Baiturganova
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Zhadyra N. Alimbayeva
;
Zh. N. Issabekov
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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Development and Pilot Evaluation of a Wearable 12-Lead ECG System for Multilead Feature Analysis in Individuals with Different Glycemic Status
(Multidisciplinary Digital Publishing Institute, 2026-03-04)
Chingiz A. Alimbayev
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Zhadyra N. Alimbayeva
;
Кassymbek Ozhikenov
;
Kairat Karibayev
;
Zhansila Orynbay
Type 2 diabetes mellitus and prediabetes often develop silently and may remain undiagnosed for years. This is particularly relevant in regions where laboratory-based screening is not always readily accessible. Against this background, the present work explores whether multilead electrocardiography can provide physiologically meaningful markers potentially associated with disturbances in glucose metabolism. We developed and tested an upgraded wearable 12-lead ECG system capable of synchronized multichannel recording under controlled conditions. ECG signals were acquired in sitting and standing positions, with a sampling frequency of 500 Hz and a recording duration of one minute per posture. The hardware architecture included a high resolution analog front-end and wireless data transmission; the accompanying software provided acquisition control, preprocessing, visualization, and data storage within a unified framework. Signal processing focused on the extraction of rhythm-related and morphological parameters, with particular attention to ventricular repolarization indices. QT interval, heart rate-corrected QT (QTc), and QT dispersion (QTd) were calculated across leads, as these parameters are known to reflect heterogeneity of repolarization and autonomic influences on myocardial electrophysiology. The analysis was structured to ensure reproducible boundary detection and systematic feature formation rather than isolated parameter measurement. The study had a pilot character and included a limited and unbalanced sample (healthy n = 10; prediabetes n = 1; T2DM n = 1). For this reason, the results are presented descriptively and should be regarded as preliminary observations. In representative cases, differences in QT-related indices were noted between categories of glycemic status; however, the potential influence of age, sex, and other confounders cannot be excluded. A pilot expert comparison of T-wave end detection demonstrated close agreement between the automated algorithm and cardiologist assessment (mean ΔTend approximately -1 to -2 ms; MAE 10-24 ms). Diagnostic performance metrics such as ROC/AUC, sensitivity, and specificity were not calculated at this stage, as validation in a larger cohort with biochemical confirmation (HbA1c, OGTT) is required. The study demonstrates the technical feasibility of combining synchronized 12-lead wearable acquisition with structured multilead repolarization analysis. The proposed system should therefore be considered a research platform intended to support further clinical validation and methodological development rather than a finished screening solution.
Цитирования (OpenAlex) 2
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Electrocardiographic Signatures of Dysglycaemia: Mechanistic Foundations, Digital Biomarkers, and Artificial Intelligence for Non-Invasive Diabetes Risk Stratification
(Multidisciplinary Digital Publishing Institute, 2026-03-18)
Chingiz A. Alimbayev
;
Zhadyra N. Alimbayeva
;
Kassymbek Ozhikenov
;
Kairat Karibayev
;
Zhansila Orynbay
Diabetes mellitus is projected to affect more than 1.3 billion people worldwide by 2050, with millions remaining undiagnosed or in a prediabetic state. Cardiovascular complications account for nearly half of diabetes-related deaths, highlighting the need for scalable tools capable of identifying metabolic dysregulation before irreversible cardiac damage develops. This review synthesizes current mechanistic, clinical, and computational evidence linking diabetes to cardiac electrophysiological remodeling and examines electrocardiography (ECG) as a non-invasive modality for early detection of dysglycaemia. Chronic hyperglycaemia, insulin resistance, oxidative stress, microvascular dysfunction, and cardiac autonomic neuropathy collectively contribute to measurable ECG alterations, including QT/QTc prolongation, increased QT dispersion, changes in Tp–e indices, and reduced heart rate variability. These changes often precede overt cardiovascular disease and correlate with glycaemic burden and diabetes duration. Recent advances in signal processing and artificial intelligence have expanded the diagnostic potential of ECG. Both classical machine learning approaches and large-scale deep learning models demonstrate that ECG contains latent signatures associated with incident type 2 diabetes and glycaemic status. Despite promising results, heterogeneity in study design, limited representation of prediabetes, and lack of standardized validation frameworks remain major barriers to clinical translation. Prospective, multi-ethnic studies are needed to establish ECG-based screening as a reliable component of early diabetes detection strategies.
Цитирования (OpenAlex) 1
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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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Interpretable Machine Learning Approach for Diabetes Classification in Patients with Cardiovascular Disease
(Multidisciplinary Digital Publishing Institute, 2026-07-04)
Chingiz A. Alimbayev
;
Zhadyra N. Alimbayeva
;
Kassymbek Ozhikenov
;
Kairat Karibayev
;
Zhanat Abuova
Diabetes mellitus is strongly associated with cardiovascular dysfunction and remains one of the leading contributors to morbidity and mortality worldwide. Early identification of diabetes-related cardiovascular alterations is essential for timely risk stratification and personalized clinical management. In the present study, an interpretable machine learning framework for diabetes classification in patients with cardiovascular disease was developed using routinely available clinical, biochemical, renal, and echocardiographic parameters. A retrospective dataset consisting of 131 cardiovascular patients was included in the final analysis, comprising 65 patients with diabetes mellitus and 66 patients without diabetes. Demographic, metabolic, renal, and cardiovascular variables, including age, body mass index (BMI), glycated hemoglobin (HbA1c), glucose concentration, estimated glomerular filtration rate (eGFR), troponin level, heart rate, and left ventricular ejection fraction (EF), were included in the analysis. Multiple supervised machine learning algorithms, including Logistic Regression, Support Vector Machine (SVM), Gradient Boosting, and Random Forest, were implemented and compared using repeated stratified cross-validation. Among the evaluated models, Random Forest demonstrated the highest classification performance, achieving a mean ROC AUC of 0.880 ± 0.050. Statistical analysis revealed significantly elevated HbA1c, glucose, and troponin levels together with reduced ejection fraction values in diabetic patients. Explainable artificial intelligence analysis using SHAP and partial dependence plots identified glucose concentration, HbA1c, age, and renal function as the dominant contributors to diabetes classification. Nonlinear relationships between metabolic and cardiovascular variables were additionally observed. The obtained findings demonstrate that interpretable machine learning approaches can provide effective discrimination between diabetic and non-diabetic cardiovascular patients while maintaining clinically meaningful interpretability. The proposed framework may contribute to future intelligent clinical decision-support systems and personalized cardiovascular risk assessment strategies.
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Optimization of Low-Channel EEG Configurations and Temporal Segmentation for Motor Imagery Classification Using a Flexible EEGNet Framework
(Multidisciplinary Digital Publishing Institute, 2026-07-16)
Yelnur Tuimebay
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Chingiz A. Alimbayev
;
Zhadyra N. Alimbayeva
;
Kassymbek Ozhikenov
Motor imagery-based brain–computer interfaces (BCIs) have attracted significant attention due to their potential applications in assistive technologies, neurorehabilitation, and wearable human–machine interaction systems. However, practical implementation of EEG-based BCIs remains challenging because high-density EEG recordings increase hardware complexity, prolong setup time, and introduce substantial spatial redundancy. In addition, the performance of motor imagery classification strongly depends on preprocessing strategy and temporal segmentation parameters. Unlike previous studies that primarily focused on developing new deep learning architectures, this work proposes a systematic optimization framework for identifying practical low-channel EEG configurations by jointly analyzing channel selection, temporal segmentation, and preprocessing strategies for subject-independent motor imagery classification. This study investigates the influence of EEG channel reduction, temporal window segmentation, and baseline correction on motor imagery classification performance using EEGNet-based deep learning architectures. Experiments were conducted using the publicly available PhysioNet EEG Motor Movement/Imagery dataset under subject-independent evaluation conditions. Several EEG configurations were analyzed, including full-scale 64-channel recordings and reduced 15-, 6-, 3-, and 2-channel motor-cortex setups. The obtained results demonstrate that reduced-channel EEG configurations can achieve performance comparable to full-scale recordings. The best classification accuracy of 65.04% was achieved using a 15-channel motor configuration combined with 2 s sliding-window segmentation and baseline correction, achieving performance comparable to the conventional 64-channel setup (64.76%), while substantially reducing the number of electrodes and hardware complexity. Statistical analysis confirmed that the difference between the two configurations was not significant (paired t-test, p = 0.1684). Furthermore, compact 3-channel configurations maintained classification accuracy above 60%, supporting the feasibility of lightweight wearable EEG systems for practical BCI applications. The experiments additionally revealed that shorter temporal windows improve classification stability and reduce susceptibility to unrelated background EEG activity. Baseline correction significantly improved model generalization by compensating for inter-trial signal variability and slow EEG drift. Overall, the findings of this study demonstrate that careful optimization of electrode selection and preprocessing strategies can substantially improve the practicality of lightweight EEG-based motor imagery classification systems while reducing hardware complexity and preserving competitive performance.

Қазақ ұлттық қыздар педагогикалық университеті / Казахский национальный женский педагогический университет

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