Translation. Region: Russian Federation –
Source: Peter the Great St. Petersburg Polytechnic University –
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Scientists from the Almazov National Medical Research Center and Peter the Great St. Petersburg Polytechnic University have presented a unique hardware and software system capable of assessing the state of cerebral autoregulation—a key mechanism that protects the brain from blood pressure fluctuations—in real time. This development, which has no direct analogues anywhere in the world, will allow physicians in intensive care and neurosurgery departments to instantly obtain critical data on brain blood flow and promptly adjust treatment, potentially saving the lives of patients with strokes, traumatic brain injuries, and other severe pathologies. The results of the study are presented in an international scientific journal. Sensors.
Cerebral autoregulation (CA) is a mechanism that maintains stable blood flow in the cerebral vessels despite a decrease or increase in a person's blood pressure. This "autopilot" can malfunction, for example, after a stroke or severe traumatic brain injury. Current noninvasive methods for assessing CA require post-processing of data, which is time-consuming—two to three hours to collect, process, and analyze the information. Transforming therapeutic approaches requires obtaining data on the state of CA in real time, directly during the examination. This allows for the recording of CA indicators over time, which is especially valuable when conducting functional tests and monitoring patients' condition.
To address the problem of non-invasive, real-time assessment of the central nervous system, a team of scientists from the A. L. Polenov Russian Neurosurgical Research Institute, a branch of the V. A. Almazov National Medical Research Center, and Peter the Great St. Petersburg Polytechnic University have developed a world-class hardware and software system (HSS) for the first time in Russia. The team includes programmers Professor Galina Malykhina and Associate Professor Vyacheslav Salnikov, mathematician and professor Valery Antonov, engineer Boris Govorov, and physicians Grigory Panuntsev, Anna Nikiforova, and Anastasia Vesnina. The research team is led by pathophysiologist, Honored Scientist of the Russian Federation, and laureate of the Russian Federation State Prize for Science and Technology, Professor Vladimir Semenyutin.
In intensive care settings, the use of a CAP for rapid assessment of the cerebral circulation in patients with severe brain injury significantly accelerates the decision-making process for physicians. This is crucial for timely adjustment of cerebral perfusion pressure, which is a priority in the effective treatment of cerebral edema, secondary ischemia, and recurrent hemorrhages, noted Professor Vladimir Semenyutin, Head of the Research Laboratory of Cerebrovascular Pathology at the Almazov National Medical Research Center of the Russian Ministry of Health.
The operating principle is based on monitoring very slow, spontaneous fluctuations in blood pressure and linear blood flow velocity in the middle cerebral arteries. These are recorded using non-invasive methods—photoplethysmography and transcranial Doppler ultrasound. The key indicator is the phase shift (the difference in rhythm) between these two "pulses" in a specific low-frequency range, the so-called Mayer waves.
The scientists' key innovation is specialized mathematical algorithms that analyze these signals not afterward, but directly during the study. The system utilizes two powerful data processing methods: short-time Fourier transform and wavelet analysis (continuous wavelet transform). The latter method, according to the study, proved more sensitive and allows for better detection of the moments when autoregulation is activated or deactivated, providing higher resolution in time and frequency. All processing occurs so quickly that the results are displayed on the screen almost instantly.
The effectiveness and safety of the complex have been confirmed by clinical trials. In the first phase, it was tested on 40 healthy volunteers. They underwent standard functional tests—hypercapnia (inhalation of air with elevated CO2 levels) and hypocapnia (intensive breathing). These tests consistently alter cerebral vascular tone, which the complex recorded, demonstrating predictable changes in phase shift. The AAC was then tested on 60 patients with various neurovascular pathologies, including atherosclerotic carotid stenosis and cerebral arteriovenous malformations. These patients exhibited asymmetry in CA values between the cerebral hemispheres, and their responses to functional tests often deviated from the norm. For example, a patient with an arteriovenous malformation did not show a normal vascular response to carbon dioxide. All this proves that the complex is capable of not only recording the functioning of a healthy system, but also clearly identifying its disturbances in pathologies.
The developed hardware and software system has demonstrated high efficiency and informativeness. It can be used both for real-time diagnostics of the cerebral circulation in patients and for studying the mechanisms regulating cerebral blood flow in healthy individuals. The proposed algorithms minimize the risk of methodological errors and significantly reduce the time required to obtain information, which is especially important for making urgent decisions, noted Galina Malykhina, professor at the Higher School of Computer Technologies and Information Systems at the Institute of Computer Science and Cybersecurity at SPbPU.
The introduction of this system into clinical practice opens a new era in bedside monitoring of critically ill patients. Currently, dozens of parameters are monitored in real time in intensive care units, including blood pressure, pulse rate, oxygen saturation, and intracranial pressure. However, a key parameter—the adequacy of cerebral blood flow—remained unnoticed due to the difficulty of instantaneous assessment. The new APC integrates into this system, providing physicians with a pathogenetically based tool for personalized management of cerebral perfusion pressure. This means that therapy—for example, the selection of medications to increase or decrease blood pressure—can be based not on average standards, but on precise data on how a specific patient's blood vessels are protecting their brain at a given moment.
The scientists aren't resting on their laurels. The next step is integrating artificial intelligence into the system for in-depth data analysis. The goal is not only to diagnose the current condition but also to predict the risk of secondary vascular complications in neurosurgical patients. The use of artificial intelligence will not only allow for the early detection of functional abnormalities, when they are still treatable, but also for more accurate determination of indications for surgical treatment.
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