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The aim of this study is to create highly effective QRS-detector of electrocardiographic (ECG) signal based on the multiresolution wavelet analysis, set of nonlinear transforms and adaptive thresholding. The efficiency of various QRS-waves detectors for processing model ECG signals contaminated by artificially simulated intensive noise and artifacts was researched. The performance of the proposed method as well as some other well-known algorithms for QRS-waves detection was further verified for clinical ECG recordings from the Physionet MIT-BIH Arrhythmia database.