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

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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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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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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
;
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.
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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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