Quasi-periodic blood flow signals, obtained by means of Doppler ultrasound techniques during graft verification, may be contaminated with random distortions (artifacts not periodical not homogeneous and affecting only some cycles in the signal). These distortions are not distributed regularly and cannot be characterized statistically or modelled with a known probability distribution function. Also, it is not possible to estimate when or where they will be presented and can cause the major deformations in the cycles where they occur, including the total loss of cycle morphology. In this paper, an improved analysis method to detect the presence of such distortions is proposed. It is based on a modified mean square error method to identify the affected cycles and then, it uses the well-known and widely used method Dynamic Time Warping for reducing the false positives detection. The identification, in time domain, of the affected cycles, and their exclusion of the signal analysis, allows to estimate parameters and extract the clinically useful information needed for a correct characterization of the blood vessel and to improve the results of coronary revascularization procedures. We tested the proposed algorithm in real signals and to evaluate the results we compute the pulsatility index. The results improve the false positive reduction comparing with the method based only in the modified mean square error reported previously.