Computational Models of Digital Personalized Medicine
Abstract
The development of personalized medicine is determined by the synergy of scientists from several fields of medicine, mathematics and computer science. Approaches based on modern methods of measurement, signal processing and machine learning complement the main methods of studying biological processes, allow us to identify the mechanisms of the disease and personalize the treatment strategy. The article is devoted to the development of a new method for monitoring the system of autoregulation of cerebral circulation in patients in real time. The process of cerebral autoregulation is determined by the signals of blood flow velocity in the arteries of the base of the brain and systemic arterial pressure in the range of Mayer waves, recorded by non-invasive methods of photoplethysmography and transcranial dopplerography. The existing methods are based on the cross-correlation function, the phase shift function between the signals of blood flow velocity and pressure, or on the assessment of the transfer function of the autoregulation system. The article proposes to use fractal methods that use the calculation of the Hölder multifractal spectra of signals and the determination of the correlation dimension of the system. The advantage of fractal methods is that they can be applied to scale-invariant signals. The combination of multifractal and traditional spectral-correlation methods in the information-measuring system will improve the quality of monitoring of cerebral autoregulation directly at the patient's bedside, take into account the characteristics of the patient and, thereby, develop the principle of personalized medicine.
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