An interpretable ECG-based approach for detecting hemodynamically significant arrhythmias using lightweight machine learning models

Authors

DOI:

https://doi.org/10.15587/1729-4061.2025.340493

Keywords:

ECG-based EF estimation, ejection fraction, machine learning, ECG classification, tQRS/tRR ratio

Abstract

The object of this study is the diagnostic process of patients with suspected hemodynamically significant arrhythmia in emergency and telemedicine settings, where rapid and interpretable decision support is required. The problem addressed is the limited access to echocardiographic assessment in emergency and resource-constrained environments, where interpretable and computationally efficient alternatives are urgently needed, particularly for mobile and field-deployed applications.

The main results show that machine learning models, such as XGBoost, achieved strong diagnostic performance (F1-score = 0.84, AUC = 0.91), while rule-based classifiers provided clinically interpretable accuracy. These results enabled partial compensation for the absence of echocardiography and contributed to reliable triage in acute and time-sensitive settings.

This effectiveness stems from key features of the method: reliance on interpretable ECG features (tQRS, tRR, HR, and EF derived from tQRS/tRR) and low computational complexity, setting it apart from more opaque deep learning methods. The results are explained by the strong correlation between these features and both electrical and mechanical heart function, enabling hemodynamic assessment without imaging. This supports clinical trust in the algorithm’s outputs.

The proposed approach is applicable in primary screening, emergency triage, telemedicine, and remote monitoring, combining accuracy with explainability and autonomy from imaging tools. Therefore, research on interpretable ECG-based detection of hemodynamically significant arrhythmias remains highly relevant, especially in settings lacking access to specialized diagnostics

Author Biographies

Ainur Bekbay, Satbayev University

Master of Technical Sciences, Senior Lecturer, Doctoral Student

Department of Robotics and Automation Equipment

Lashin Bazarbay, Satbayev University

Master of Technical Sciences, Senior Lecturer

Department of Robotics and Automation Equipment

Zhanar Bigaliyeva, Satbayev University

Master of Technical Sciences, Senior Lecturer

Department of Robotics and Automation Equipment

Vinera Baiturganova, Satbayev University

Master of Technical Sciences, Senior Lecturer

Department of Robotics and Automation Equipment

Akezhan Sabibolda, Institute of Mechanics and Mechanical Engineering named after Academician U. A. Dzholdasbekov; Almaty Academy of Ministry of Internal Affairs

PhD

Department of Cyber Security and Information Technology

Yersaiyn Mailybayev, International University of Transportation and Humanities

PhD

Vice-Rector for Science and Digitalization

Nurzhigit Smailov, Institute of Mechanics and Mechanical Engineering named after Academician U. A. Dzholdasbekov; Satbayev University

PhD, Senior Scientist

Department of Electronics, Telecommunication and Space Technologies

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An interpretable ECG-based approach for detecting hemodynamically significant arrhythmias using lightweight machine learning models

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Published

2025-10-28

How to Cite

Bekbay, A., Bazarbay, L., Bigaliyeva, Z., Baiturganova, V., Sabibolda, A., Mailybayev, Y., & Smailov, N. (2025). An interpretable ECG-based approach for detecting hemodynamically significant arrhythmias using lightweight machine learning models. Eastern-European Journal of Enterprise Technologies, 5(9 (137), 117–124. https://doi.org/10.15587/1729-4061.2025.340493

Issue

Vol. 5 No. 9 (137) (2025): Information and controlling system

Section

Information and controlling system

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Copyright (c) 2025 Ainur Bekbay, Lashin Bazarbay, Zhanar Bigaliyeva, Vinera Baiturganova, Akezhan Sabibolda, Yersaiyn Mailybayev, Nurzhigit Smailov

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