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