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The study proposed a method and an algorithm for intelligent signal processing of blood flow velocity and system blood pressure in the middle cerebral arteries, which is intended for integrated platform solutions in digital medicine. The development and application of such a system are aimed at supporting the concept of digital medicine. This article presents a practical step-by-step algorithm of wavelet analysis for processing blood flow velocity and blood pressure signals to assess the state of cerebral circulatory autoregulation. Realtime monitoring of the state of cerebral blood flow autoregulation of patients will allow doctors to adjust the treatment faster. The study proposes a numerical method for analysis in the range of Meyer waves of system fluctuations and cerebral hemodynamics of healthy volunteers and patients with cerebral arteriovenous malformations in order to determine signs of cerebral auto-regulation disorders. The numerical method for signal processing of cerebral blood pressure and blood flow velocity in the middle cerebral arteries is using continuous wavelet transform to calculate the cross-spectrum of analyzed signals, wavelet coherence, and phase shift between them. The results of this work proved that cerebral autoregulation disturbances could be determined by the dependence of two parameters: the coherence value and the phase angle between the signals.