{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,4]],"date-time":"2025-10-04T04:40:40Z","timestamp":1759552840374,"version":"build-2065373602"},"reference-count":33,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2025,10,2]],"date-time":"2025-10-02T00:00:00Z","timestamp":1759363200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003443","name":"Ministry of Education and Science of the Russian Federation","doi-asserted-by":"crossref","award":["075-15-2025-674"],"award-info":[{"award-number":["075-15-2025-674"]}],"id":[{"id":"10.13039\/501100003443","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["www.mdpi.com"],"crossmark-restriction":true},"short-container-title":["Sensors"],"abstract":"<jats:p>The phase shift (PS) between spontaneous slow oscillations of cerebral and systemic hemodynamics reliably reflects the state of cerebral autoregulation (CA). However, CA measurements are performed retrospectively after studying the signals from the analysis sensors. At the same time, CA-oriented therapy is becoming increasingly important with the receipt of data on the state of CA in real time, especially in intensive care units. We offer a hardware and software complex for transcranial Dopplerography, which uses a non-invasive method and allows for continuous measurement of cerebral blood flow to assess the rate of CA in real time. The hardware and software complex uses sensors to measure the PS between spontaneous slow oscillations of blood flow velocity (BFV) in the middle cerebral arteries (MCAs) and systemic arterial pressure (BP) in the Mayer wave range and performs wavelet analysis of sensor signals. An examination of 30 volunteers, with an average age of 28 \u00b1 8 years, and 15 patients, with an average age of 57 \u00b1 16 years, with various neurovascular pathologies confirms the feasibility of using the developed hardware and software complex for continuous monitoring of PS in real time to study the mechanisms of cerebral blood flow regulation.<\/jats:p>","DOI":"10.3390\/s25196060","type":"journal-article","created":{"date-parts":[[2025,10,2]],"date-time":"2025-10-02T08:20:28Z","timestamp":1759393228000},"page":"6060","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Software and Hardware Complex for Assessment of Cerebral Autoregulation in Real Time"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9248-9821","authenticated-orcid":false,"given":"Vladimir","family":"Semenyutin","sequence":"first","affiliation":[{"name":"Almazov National Medical Research Centre, Saint Petersburg 197341, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2248-3192","authenticated-orcid":false,"given":"Valeriy","family":"Antonov","sequence":"additional","affiliation":[{"name":"Institute of Computer Science and Cybersecurity, Graduate School of Computer Technologies and Information Systems, Peter the Great St. Petersburg Polytechnic University, Saint Petersburg 195251, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1026-8727","authenticated-orcid":false,"given":"Galina","family":"Malykhina","sequence":"additional","affiliation":[{"name":"Institute of Computer Science and Cybersecurity, Graduate School of Computer Technologies and Information Systems, Peter the Great St. Petersburg Polytechnic University, Saint Petersburg 195251, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2617-6739","authenticated-orcid":false,"given":"Anna","family":"Nikiforova","sequence":"additional","affiliation":[{"name":"Almazov National Medical Research Centre, Saint Petersburg 197341, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0983-1831","authenticated-orcid":false,"given":"Grigory","family":"Panuntsev","sequence":"additional","affiliation":[{"name":"Almazov National Medical Research Centre, Saint Petersburg 197341, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3115-207X","authenticated-orcid":false,"given":"Vyacheslav","family":"Salnikov","sequence":"additional","affiliation":[{"name":"Institute of Computer Science and Cybersecurity, Graduate School of Computer Technologies and Information Systems, Peter the Great St. Petersburg Polytechnic University, Saint Petersburg 195251, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7452-0944","authenticated-orcid":false,"given":"Anastasiya","family":"Vesnina","sequence":"additional","affiliation":[{"name":"Almazov National Medical Research Centre, Saint Petersburg 197341, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1001\/archneur.1973.00490210023001","article-title":"Quantitative evaluation of normal and pathologic cerebral blood regulation to perfusion pressure: Changes in man","volume":"28","author":"Olesen","year":"1973","journal-title":"Arch. Neurol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"769","DOI":"10.3171\/jns.1982.57.6.0769","article-title":"Noninvasive transcranial Doppler ultrasound recording of flow velocity in basal cerebral arteries","volume":"57","author":"Aaslid","year":"1982","journal-title":"J. Neurosurg."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1161\/01.STR.20.1.1","article-title":"Validity of cerebral arterial blood flow calculations from velocity measurements","volume":"20","author":"Kontos","year":"1989","journal-title":"Stroke"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1016\/S1042-3680(18)30807-6","article-title":"Transcranial Doppler in cerebral vasospasm","volume":"1","author":"Newell","year":"1990","journal-title":"Neurosurg. Clin. N. Am."},{"key":"ref_5","first-page":"369","article-title":"Transcranial Doppler identification of changing autoregulatory thresholds after autoregulatory impartment","volume":"48","author":"Lewis","year":"2001","journal-title":"Neurosurgery"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1007\/BF03356593","article-title":"A computing system for the clinical and experimental investigation of cerebrovascular reactivity","volume":"14","author":"Smielewski","year":"1997","journal-title":"Int. J. Clin. Monit. Comput."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"402","DOI":"10.1159\/000072564","article-title":"Mechanisms underlying phase lag between systemic arterial blood pressure and cerebral blood flow velocity","volume":"16","author":"Kuo","year":"2003","journal-title":"Cerebrovasc. Dis."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1133","DOI":"10.1213\/01.ANE.0000111101.41190.99","article-title":"Continuous assessment of cerebral autoregulation in subarachnoid hemorrhage","volume":"98","author":"Soehle","year":"2004","journal-title":"Anesth. Analg."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1071","DOI":"10.1038\/jcbfm.2008.13","article-title":"Cerebral autoregulation on overview of current concepts and methodology with special focus on the elderly","volume":"28","author":"Claassen","year":"2008","journal-title":"J. Cereb. Blood Flow Metab."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1007\/s10286-009-0011-8","article-title":"Transcranial Doppler for evaluation of cerebral autoregulation. Cerebral autoregulation dynamics in premature newborns","volume":"19","author":"Panerai","year":"2009","journal-title":"Clin. Auton. Res."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1575","DOI":"10.1007\/s00701-012-1423-0","article-title":"Long pressure reactivity index (L-PRx) as a measure of autoregulation correlates with outcomes in traumatic brain injury patients","volume":"154","author":"Porras","year":"2012","journal-title":"Acta Neurochir."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Thudium, M., Moestl, S., Hoffmann, F., Hoff, A., Kornilov, E., Heusser, K., Tank, J., and Soehle, M. (2023). Cerebral blood flow autoregulation assessment by correlation analysis between mean arterial blood pressure and transcranial doppler sonography or near infrared spectroscopy is different: A pilot study. PLoS ONE, 18.","DOI":"10.1371\/journal.pone.0287578"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"577","DOI":"10.1109\/TBME.2024.3463873","article-title":"Modeling the Mechanisms of Non-Neurogenic Dynamic Cerebral Autoregulation","volume":"72","author":"Banerjee","year":"2025","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1814","DOI":"10.1161\/01.CIR.0000031798.07790.FE","article-title":"Autonomic Neural Control of Dynamic Cerebral Autoregulation in Humans","volume":"106","author":"Zhang","year":"2002","journal-title":"Circulation"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"665","DOI":"10.1177\/0271678X15626425","article-title":"Transfer function analysis of dynamic cerebral autoregulation research network","volume":"36","author":"Claassen","year":"2016","journal-title":"J. Cereb. Blood Flow Metab."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Pan, Y., Wan, W., Xiang, M., and Guan, Y. (2022). Transcranial Doppler Ultrasonography as a Diagnostic Tool for Cerebrovascular Disorders. Front. Hum. Neurosci., 16.","DOI":"10.3389\/fnhum.2022.841809"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"065006","DOI":"10.1088\/1361-6579\/ad548d","article-title":"Determinants of the dynamic cerebral critical closing pressure response to changes in mean arterial pressure","volume":"45","author":"Panerai","year":"2024","journal-title":"Physiol. Meas."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1711","DOI":"10.1007\/s10877-022-00817-1","article-title":"Validity of transcranial Doppler ultrasonography-determined dynamic cerebral autoregulation estimated using transfer function analysis","volume":"36","author":"Watanabe","year":"2022","journal-title":"J. Clin. Monit. Comput."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1177\/0271678X221119760","article-title":"Cerebrovascular Research Network (CARNet). Transfer function analysis of dynamic cerebral autoregulation: A CARNet white paper 2022 update","volume":"43","author":"Panerai","year":"2023","journal-title":"J. Cereb. Blood Flow Metab."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Liu, X., Hu, X., Brady, K.M., Koehler, R., Smielewski, P., Czosnyka, M., Donnelly, J., and Lee, J.K. (2020). Comparison of wavelet and correlation indices of cerebral autoregulation in a pediatric swine model of cardiac arrest. Sci. Rep., 10.","DOI":"10.1038\/s41598-020-62435-8"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.nicl.2016.01.020","article-title":"Wavelet coherence analysis of dynamic cerebral autoregulation in neonatal hypoxic\u2013ischemic encephalopathy","volume":"11","author":"Tian","year":"2016","journal-title":"NeuroImage Clin."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Liu, X., Tekes, A., Perin, J., Chen, M., Soares, S., Massaro, A., Govindan, R., Parkinson, C., Chavez-Valdez, R., and Northington, F. (2021). Wavelet Autoregulation Monitoring Identifies Blood Pressures Associated with Brain Injury in Neonatal Hypoxic-Ischemic Encephalopathy. Front. Neurol., 12.","DOI":"10.3389\/fneur.2021.662839"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Malykhina, G., Salnikov, V., Semenyutin, V., and Tarkhov, D. (2020, January 22\u201323). Digitalization of medical services for detecting violations of cerebrovascular regulation based on a neural network signal analysis algorithm. Proceedings of the SPBPU IDE\u201920: Proceedings of the 2nd International Scientific Conference on Innovations in Digital Economy, Saint Petersburg, Russia.","DOI":"10.1145\/3444465.3444526"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Li, S., Ma, S., and Wang, S. (2023). Optimal Complex Morlet Wavelet Parameters for Quantitative Time-Frequency Analysis of Molecular Vibration. Appl. Sci., 13.","DOI":"10.3390\/app13042734"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Rhif, M., Ben Abbes, A., Farah, I., Martinez, B., and Sang, Y. (2019). Wavelet transform application for\/in Non-Stationary Triesime-Se Analysis: A Review. Appl. Sci., 9.","DOI":"10.3390\/app9071345"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"561","DOI":"10.5194\/npg-11-561-2004","article-title":"Application of the cross wavelet transform and wavelet coherence to geophysical time series","volume":"11","author":"Grinsted","year":"2004","journal-title":"Nonlinear Process. Geophys."},{"key":"ref_27","first-page":"757","article-title":"About the informativity of coherent analysis in the EEG studies","volume":"59","author":"Kulaichev","year":"2009","journal-title":"J. High. Nerv. Act."},{"key":"ref_28","unstructured":"Malykhina, G., Salnikov, V., and Semenyutin, V. (2020, January 12\u201316). Neural network algorithm for the diagnosis of impaired cerebral autoregulation. Proceedings of the International Conference on Neuroinformatics 2020, Moscow, Russia."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Semenyutin, V., Antonov, V., Malykhina, G., and Salnikov, V. (2022). Investigation of Cerebral Autoregulation Using Time-Frequency Transformations. Biomedicines, 10.","DOI":"10.3390\/biomedicines10123057"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1951","DOI":"10.1161\/STROKEAHA.109.575159","article-title":"Real-time continuous monitoring of cerebral blood flow autoregulation using near-infrared spectroscopy in patients undergoing cardiopulmonary bypass","volume":"41","author":"Brady","year":"2010","journal-title":"Stroke"},{"key":"ref_31","unstructured":"Nasyrov, A. (2025, September 21). Application of the Empirical Mode Decomposition Method for Processing Complex Signals. \u201cModern Problems of Remote Sensing, Radar, Wave Propagation and Diffraction\u201d. Murom, 2024. (In Russian)."},{"key":"ref_32","first-page":"873","article-title":"Empirical Mode Decomposition: Theory & Applications","volume":"7","author":"Maheshwari","year":"2024","journal-title":"Int. J. Electron. Electr. Eng."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Antonov, V.I., Malykhina, G.F., and Semenyutin, V.B. (2022, January 19\u201321). Measuring and computing models of cerebral autoregulation for digital personalized medicine. Proceedings of the IMEKO TC6 International Conference on Metrology and Digital Transformation, Berlin, Germany. Available online: https:\/\/www.proceedings.com\/content\/066\/066087webtoc.pdf.","DOI":"10.21014\/tc6-2022.008"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/19\/6060\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,4]],"date-time":"2025-10-04T04:16:06Z","timestamp":1759551366000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/19\/6060"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,2]]},"references-count":33,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2025,10]]}},"alternative-id":["s25196060"],"URL":"https:\/\/doi.org\/10.3390\/s25196060","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2025,10,2]]}}}