Article · Bulletin of Shakarim University Technical Sciences · January 25, 2026

PHOTOPLETHYSMOGRAPHY-BASED HEART RATE VARIABILITY ANALYSIS USING MACHINE LEARNING METHODS FOR NON-INVASIVE SCREENING OF CORONARY ARTERY DISEASE WITH THE ZHUREK IOT DEVICE

Zh. Y. Baigarayeva, Assiya Boltaboyeva, Баглан Иманбек, Mergul Kozhamberdiyeva, Aiman Bekturganova

Abstract

Cardiovascular diseases remain the leading cause of mortality worldwide; therefore, early-stage noninvasive risk assessment is crucial both for prevention and for the efficient allocation of healthcare resources. Heart rate variability (HRV) is a reliable indicator of autonomic balance, yet in clinical practice it still relies mainly on long-term ECG recordings, which limits large-scale application. Although photoplethysmography (PPG) is widely available, the effectiveness of PPG-derived HRV metrics for detecting ischemic heart disease (IHD) has not been fully established.In this study, PPG signals obtained from a low-cost Zhurek device, combined with machine learning classifiers, enabled IHD detection with an accuracy of 90.82%. Comparison with three-channel Holter ECG showed satisfactory agreement. SHAP and mutual information analyses highlighted the dominant role of frequency-domain features (HF, LF). Data balancing using CTGAN improved training stability.Overall, the results demonstrate that PPG-based HRV analysis offers a feasible, accessible, and interpretable approach for IHD screening. Future steps include multicenter validation, expansion of feature sets, and integration of the method into wearable devices.

Non-Invasive Vital Sign MonitoringHeart Rate Variability and Autonomic ControlECG Monitoring and Analysisphotoplethysmographyheart rate variabilityischemic heart disease

PHOTOPLETHYSMOGRAPHY-BASED HEART RATE VARIABILITY ANALYSIS USING MACHINE LEARNING METHODS FOR NON-INVASIVE SCREENING OF CORONARY ARTERY DISEASE WITH THE ZHUREK IOT DEVICE

Abstract

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