AN INTERPRETABLE ECG-BASED APPROACH FOR DETECTING HEMODYNAMICALLY SIGNIFICANT ARRHYTHMIAS USING LIGHTWEIGHT MACHINE LEARNING MODELS
Bekbay A. Bazarbay L. Bigaliyeva Z. Baiturganova V. Sabibolda A. Mailybayev Y. Smailov N.
2025Technology Center
Eastern-European Journal of Enterprise Technologies
2025#5Issue 9117 - 124 pp.
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. Copyright
ECG classification , ECG-based EF estimation , ejection fraction , machine learning , tQRS/tRR ratio
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Department of Robotics and Automation Equipment, Satbayev University, Satbaev str., 22, Almaty, 050013, Kazakhstan
Department of Robotics and Automation Equipment, Satbayev University, Satbaev str., 22, Almaty, 050013, Kazakhstan
Institute of Mechanics and Mechanical Engineering named after Academician U. A. Dzholdasbekov, Kurmangazy str., 29, Almaty, 050010, Kazakhstan
Department of Cyber Security and Information Technology, Almaty Academy of Ministry of Internal Affairs, Utepov str., 29, Almaty, 050060, Kazakhstan
International University of Transportation and Humanities, micro-district Zhetysu-1, 32a, Almaty, 050063, Kazakhstan
Department of Radio Engineering, Electronics and Space Technologies, Satbayev University, Satbaev str., 22, Almaty, 050013, Kazakhstan
Department of Robotics and Automation Equipment
Department of Robotics and Automation Equipment
Institute of Mechanics and Mechanical Engineering named after Academician U. A. Dzholdasbekov
Department of Cyber Security and Information Technology
International University of Transportation and Humanities
Department of Radio Engineering
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