Digitalization of medical services for detecting violations of cerebrovascular regulation based on a neural network signal analysis algorithm

When solving the problem of predicting impaired autoregulation of blood circulation in the brain, scientists usually use a prognostic expression that interconnects the function of coherence of blood flow velocities in the arteries and atrerial pressure with a phase shift in the M-wave range. In our study we proposed to employ neural networks to adapt the method to specific patients or to a group of patients. A neural network algorithm has been developed to identify in the statistical properties of coherent biological signals present in a mixture with other signals and interference. The algorithm includes real-time determination of the coherence function of signals between fluctuations in systemic blood pressure and blood flow velocities in the left and right middle cerebral arteries and the phase shift function between these signals in the Mayer wavelength range. To reduce the influence of noise, it is proposed to use the technique of a sliding frame, divided into windows. The coherence and phase shift functions obtained in the windows are averagedwithin the frame boundaries. As a result, smoothed functions can be obtained in the time-frequency domain. To detect infractions of the cerebral autoregulation process, it is proposed to use trained neural feedforward network, which generalizing property can be improved as new experimental data are obtained while maintaining a balance between individual and general characteristics of patients.