Accurate detection of QRS complex in electrocardiogram (ECG) signals is essential for reliable estimation of the heart rate. However, traditional QRS detection algorithms often have low performance in the presence of various types of noise and signal abnormalities and require additional memory resources to track undetected peaks. In this work, we propose a novel QRS complex detection algorithm based on the root mean square (RMS) shifting concept. The concept of RMS shifting consists to remove an amount proportional to the RMS of the pre-processed signal for moving all the P and T waves of the electrocardiogram toward the negative part of the y-axis and keep only the R peaks in the positive part. Then, all the roots can be softly detected using the corollary of the intermediate value theorem known as Bolzano’s theorem. The detection of R peaks is ensured by Rolle’s theorem. Our proposed model has been implemented and evaluated on a diverse set of ECG datasets and its performances are comparable to that of spectral analysis based on the FFT algorithm widely used nowadays. For the construction of this model, we used a sample of an electrocardiogram signal from the MIT/BIH Arrhythmia database stored and provided by Simulink. The peaks detected by our algorithm have been verified and confirmed by the well-known Pan-Tompkins Algorithm used by MATLAB. Then, Our model has been applied to a publicly shared electrocardiogram database provided by a Japanese physiological laboratory. The comparison of the heart rate estimated by the proposed method and the spectral analysis method shows a low absolute error average (0.89 bpm), a low relative error average (1.37%), a low root mean square error (1.05 bpm), and a correlation coefficient very close to 1 (0.9938). We also measured the CPU time to assess the performance of our proposed method and we found that our algorithm is twice as fast as the conventional method. Therefore, we inferred that our model is reliable for estimating heart rate for electrocardiography applications.