{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T01:59:44Z","timestamp":1778896784264,"version":"3.51.4"},"reference-count":120,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2024,2,23]],"date-time":"2024-02-23T00:00:00Z","timestamp":1708646400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council","doi-asserted-by":"publisher","award":["ALLRP-576386-22"],"award-info":[{"award-number":["ALLRP-576386-22"]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council","doi-asserted-by":"publisher","award":["ALLRP-586244-23"],"award-info":[{"award-number":["ALLRP-586244-23"]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The modeling and forecasting of cerebral pressure\u2013flow dynamics in the time\u2013frequency domain have promising implications for veterinary and human life sciences research, enhancing clinical care by predicting cerebral blood flow (CBF)\/perfusion, nutrient delivery, and intracranial pressure (ICP)\/compliance behavior in advance. Despite its potential, the literature lacks coherence regarding the optimal model type, structure, data streams, and performance. This systematic scoping review comprehensively examines the current landscape of cerebral physiological time-series modeling and forecasting. It focuses on temporally resolved cerebral pressure\u2013flow and oxygen delivery data streams obtained from invasive\/non-invasive cerebral sensors. A thorough search of databases identified 88 studies for evaluation, covering diverse cerebral physiologic signals from healthy volunteers, patients with various conditions, and animal subjects. Methodologies range from traditional statistical time-series analysis to innovative machine learning algorithms. A total of 30 studies in healthy cohorts and 23 studies in patient cohorts with traumatic brain injury (TBI) concentrated on modeling CBFv and predicting ICP, respectively. Animal studies exclusively analyzed CBF\/CBFv. Of the 88 studies, 65 predominantly used traditional statistical time-series analysis, with transfer function analysis (TFA), wavelet analysis, and autoregressive (AR) models being prominent. Among machine learning algorithms, support vector machine (SVM) was widely utilized, and decision trees showed promise, especially in ICP prediction. Nonlinear models and multi-input models were prevalent, emphasizing the significance of multivariate modeling and forecasting. This review clarifies knowledge gaps and sets the stage for future research to advance cerebral physiologic signal analysis, benefiting neurocritical care applications.<\/jats:p>","DOI":"10.3390\/s24051453","type":"journal-article","created":{"date-parts":[[2024,2,23]],"date-time":"2024-02-23T10:47:30Z","timestamp":1708685250000},"page":"1453","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Time-Series Modeling and Forecasting of Cerebral Pressure\u2013Flow Physiology: A Scoping Systematic Review of the Human and Animal Literature"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2764-145X","authenticated-orcid":false,"given":"Nuray","family":"Vakitbilir","sequence":"first","affiliation":[{"name":"Biomedical Engineering, Price Faculty of Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Logan","family":"Froese","sequence":"additional","affiliation":[{"name":"Biomedical Engineering, Price Faculty of Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3737-2065","authenticated-orcid":false,"given":"Alwyn","family":"Gomez","sequence":"additional","affiliation":[{"name":"Section of Neurosurgery, Department of Surgery, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB R3A 1R9, Canada"},{"name":"Department of Human Anatomy and Cell Science, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB R3E 0J9, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3231-5683","authenticated-orcid":false,"given":"Amanjyot Singh","family":"Sainbhi","sequence":"additional","affiliation":[{"name":"Biomedical Engineering, Price Faculty of Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kevin Y.","family":"Stein","sequence":"additional","affiliation":[{"name":"Biomedical Engineering, Price Faculty of Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abrar","family":"Islam","sequence":"additional","affiliation":[{"name":"Biomedical Engineering, Price Faculty of Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tobias J. G.","family":"Bergmann","sequence":"additional","affiliation":[{"name":"Undergraduate Engineering, Price Faculty of Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Izabella","family":"Marquez","sequence":"additional","affiliation":[{"name":"Undergraduate Engineering, Price Faculty of Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fiorella","family":"Amenta","sequence":"additional","affiliation":[{"name":"Undergraduate Engineering, Price Faculty of Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Younis","family":"Ibrahim","sequence":"additional","affiliation":[{"name":"Section of Neurosurgery, Department of Surgery, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB R3A 1R9, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1737-0510","authenticated-orcid":false,"given":"Frederick A.","family":"Zeiler","sequence":"additional","affiliation":[{"name":"Biomedical Engineering, Price Faculty of Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, Canada"},{"name":"Section of Neurosurgery, Department of Surgery, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB R3A 1R9, Canada"},{"name":"Department of Clinical Neuroscience, Karolinska Institutet, 171 77 Stockholm, Sweden"},{"name":"Division of Anesthesia, Department of Medicine, Addenbrooke\u2019s Hospital, University of Cambridge, Cambridge CB2 0QQ, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Addison, P.S. (2015, January 25\u201329). Identifying Stable Phase Coupling Associated with Cerebral Autoregulation Using the Synchrosqueezed Cross-Wavelet Transform and Low Oscillation Morlet Wavelets. Proceedings of the 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Milan, Italy.","DOI":"10.1109\/EMBC.2015.7319749"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1559","DOI":"10.1089\/neu.2017.5596","article-title":"Estimating Pressure Reactivity Using Noninvasive Doppler-Based Systolic Flow Index","volume":"35","author":"Zeiler","year":"2018","journal-title":"J. Neurotrauma"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1058","DOI":"10.1016\/j.medengphy.2011.11.010","article-title":"Intracranial Hypertension Prediction Using Extremely Randomized Decision Trees","volume":"34","author":"Scalzo","year":"2012","journal-title":"Med. Eng. Phys."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1251","DOI":"10.1007\/s11912-022-01280-6","article-title":"Diagnosis and Management of Stroke in Adults with Primary Brain Tumor","volume":"24","author":"Gilbert","year":"2022","journal-title":"Curr. Oncol. Rep."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"793","DOI":"10.1007\/s00401-020-02215-w","article-title":"Cerebral Blood Flow Decrease as an Early Pathological Mechanism in Alzheimer\u2019s Disease","volume":"140","author":"Korte","year":"2020","journal-title":"Acta Neuropathol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1088\/0967-3334\/19\/3\/001","article-title":"Assessment of Cerebral Pressure Autoregulation in Humans\u2014A Review of Measurement Methods","volume":"19","author":"Panerai","year":"1998","journal-title":"Physiol. Meas."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"312","DOI":"10.1186\/s13054-021-03678-3","article-title":"Monitoring and Modifying Brain Oxygenation in Patients at Risk of Hypoxic Ischaemic Brain Injury after Cardiac Arrest","volume":"25","author":"Skrifvars","year":"2021","journal-title":"Crit. Care"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1007\/s10558-007-9044-6","article-title":"Cerebral Autoregulation: From Models to Clinical Applications","volume":"8","author":"Panerai","year":"2008","journal-title":"Cardiovasc. Eng."},{"key":"ref_9","unstructured":"Silverman, A., and Petersen, N.H. (2023). StatPearls, StatPearls Publishing."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1186\/s12984-018-0456-x","article-title":"Machine Learning Algorithms for Activity Recognition in Ambulant Children and Adolescents with Cerebral Palsy","volume":"15","author":"Ahmadi","year":"2018","journal-title":"J. NeuroEng. Rehabil."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"857521","DOI":"10.3389\/fnagi.2022.857521","article-title":"Comparison of Conventional Logistic Regression and Machine Learning Methods for Predicting Delayed Cerebral Ischemia After Aneurysmal Subarachnoid Hemorrhage: A Multicentric Observational Cohort Study","volume":"14","author":"Hu","year":"2022","journal-title":"Front. Aging Neurosci."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Islam, M.S., Hussain, I., Rahman, M.M., Park, S.J., and Hossain, M.A. (2022). Explainable Artificial Intelligence Model for Stroke Prediction Using EEG Signal. Sensors, 22.","DOI":"10.3390\/s22249859"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Chac\u00f3n, M., Jara, J.L., Miranda, R., Katsogridakis, E., and Panerai, R.B. (2018). Non-Linear Models for the Detection of Impaired Cerebral Blood Flow Autoregulation. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0191825"},{"key":"ref_14","unstructured":"(2023, September 27). Cochrane Handbook Cochrane Handbook for Systematic Reviews of Interventions. Available online: https:\/\/training.cochrane.org\/handbook."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"n71","DOI":"10.1136\/bmj.n71","article-title":"The PRISMA 2020 Statement: An Updated Guideline for Reporting Systematic Reviews","volume":"372","author":"Page","year":"2021","journal-title":"BMJ"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"467","DOI":"10.7326\/M18-0850","article-title":"PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation","volume":"169","author":"Tricco","year":"2018","journal-title":"Ann. Intern. Med."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"07TR02","DOI":"10.1088\/1361-6579\/acdfb6","article-title":"Regional Disparity in Continuously Measured Time-Domain Cerebrovascular Reactivity Indices: A Scoping Review of Human Literature","volume":"44","author":"Sainbhi","year":"2023","journal-title":"Physiol. Meas."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1204874","DOI":"10.3389\/fphys.2023.1204874","article-title":"The Effect of Burst Suppression on Cerebral Blood Flow and Autoregulation: A Scoping Review of the Human and Animal Literature","volume":"14","author":"Siddiqi","year":"2023","journal-title":"Front. Physiol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"H976","DOI":"10.1152\/ajpheart.00639.2006","article-title":"Alterations in Cerebral Autoregulation and Cerebral Blood Flow Velocity during Acute Hypoxia: Rest and Exercise","volume":"292","author":"Ainslie","year":"2007","journal-title":"Am. J. Physiol. Heart Circ. Physiol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1007\/s00421-003-0965-5","article-title":"Cerebral Autoregulation Is Compromised during Simulated Fluctuations in Gravitational Stress","volume":"91","author":"Brown","year":"2004","journal-title":"Eur. J. Appl. Physiol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"e013357","DOI":"10.1136\/bmjopen-2016-013357","article-title":"Wavelet Coherence Analysis of Cerebral Oxygenation Signals Measured by Near-Infrared Spectroscopy in Sailors: An Exploratory, Experimental Study","volume":"6","author":"Bu","year":"2016","journal-title":"BMJ Open"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Bu, L., Zhang, M., Li, J., Li, F., Liu, H., and Li, Z. (2017). Effects of Sleep Deprivation on Phase Synchronization as Assessed by Wavelet Phase Coherence Analysis of Prefrontal Tissue Oxyhemoglobin Signals. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0169279"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.neulet.2018.01.026","article-title":"Effects of Poor Sleep Quality on Brain Functional Connectivity Revealed by Wavelet-Based Coherence Analysis Using NIRS Methods in Elderly Subjects","volume":"668","author":"Bu","year":"2018","journal-title":"Neurosci. Lett."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.medengphy.2010.09.023","article-title":"Non-Linear Multivariate Modeling of Cerebral Hemodynamics with Autoregressive Support Vector Machines","volume":"33","author":"Chacon","year":"2011","journal-title":"Med. Eng. Phys."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Chac\u00f3n, M., Rojas-Pescio, H., Pe\u00f1aloza, S., and Landerretche, J. (2022). Machine Learning Models and Statistical Complexity to Analyze the Effects of Posture on Cerebral Hemodynamics. Entropy, 24.","DOI":"10.3390\/e24030428"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1152\/japplphysiol.90822.2008","article-title":"Dynamic Cerebral Autoregulation during Repeated Squat-Stand Maneuvers","volume":"106","author":"Claassen","year":"2009","journal-title":"J. Appl. Physiol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1152\/japplphysiol.00100.2022","article-title":"Dynamics of the Cerebral Autoregulatory Response to Paced Hyperventilation Assessed Using Subcomponent and Time-Varying Analyses","volume":"133","author":"Clough","year":"2022","journal-title":"J. Appl. Physiol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.mvr.2014.02.008","article-title":"Wavelet Coherence Analysis of Spontaneous Oscillations in Cerebral Tissue Oxyhemoglobin Concentrations and Arterial Blood Pressure in Elderly Subjects","volume":"93","author":"Cui","year":"2014","journal-title":"Microvasc. Res."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Poon, C.-S., and Kazemi, H. (2001). Frontiers in Modeling and Control of Breathing: Integration at Molecular, Cellular, and Systems Levels, Springer. Advances in Experimental Medicine and Biology.","DOI":"10.1007\/978-1-4615-1375-9"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"R627","DOI":"10.1152\/ajpregu.00384.2003","article-title":"Two-Breath CO2 Test Detects Altered Dynamic Cerebrovascular Autoregulation and CO2 Responsiveness with Changes in Arterial Pco2","volume":"287","author":"Edwards","year":"2004","journal-title":"Am. J. Physiol. Regul. Integr. Comp. Physiol."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Gehalot, P., Mathew, A., Behbehani, K., and Zhang, R. (2005, January 17\u201318). Efficacy of Using Mean Arterial Blood Pressure Sequence for Linear Modeling of Cerebral Autoregulation. Proceedings of the 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, Shanghai, China.","DOI":"10.1109\/IEMBS.2005.1615760"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"776","DOI":"10.1038\/sj.jcbfm.9600384","article-title":"Acute Exposure to Normobaric Mild Hypoxia Alters Dynamic Relationships between Blood Pressure and Cerebral Blood Flow at Very Low Frequency","volume":"27","author":"Iwasaki","year":"2007","journal-title":"J. Cereb. Blood Flow. Metab."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1056","DOI":"10.1088\/0967-3334\/37\/7\/1056","article-title":"Revisiting the Frequency Domain: The Multiple and Partial Coherence of Cerebral Blood Flow Velocity in the Assessment of Dynamic Cerebral Autoregulation","volume":"37","author":"Katsogridakis","year":"2016","journal-title":"Physiol. Meas."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"592","DOI":"10.1016\/j.medengphy.2013.10.011","article-title":"Nonstationary Multivariate Modeling of Cerebral Autoregulation during Hypercapnia","volume":"36","author":"Kostoglou","year":"2014","journal-title":"Med. Eng. Phys."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1097\/00004647-199803000-00010","article-title":"Frequency Domain Analysis of Cerebral Blood Flow Velocity and Its Correlation with Arterial Blood Pressure","volume":"18","author":"Kuo","year":"1998","journal-title":"J. Cereb. Blood Flow. Metab."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"777","DOI":"10.1088\/0967-3334\/35\/5\/777","article-title":"Wavelet Coherence Analysis of Prefrontal Oxygenation Signals in Elderly Subjects with Hypertension","volume":"35","author":"Li","year":"2014","journal-title":"Physiol. Meas."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"647","DOI":"10.1016\/S1350-4533(03)00015-8","article-title":"Dynamic Cerebral Autoregulation Assessment Using an ARX Model: Comparative Study Using Step Response and Phase Shift Analysis","volume":"25","author":"Liu","year":"2003","journal-title":"Med. Eng. Phys."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1007\/BF02345461","article-title":"Analysis of Dynamic Cerebral Autoregulation Using an ARX Model Based on Arterial Blood Pressure and Middle Cerebral Artery Velocity Simulation","volume":"40","author":"Liu","year":"2002","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"42","DOI":"10.2174\/1874120701206010042","article-title":"Linear and Nonlinear Modeling of Cerebral Flow Autoregulation Using Principal Dynamic Modes","volume":"6","author":"Marmarelis","year":"2012","journal-title":"Open Biomed. Eng. J."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"20150180","DOI":"10.1098\/rsta.2015.0180","article-title":"Multiple-Input Nonlinear Modelling of Cerebral Haemodynamics Using Spontaneous Arterial Blood Pressure, End-Tidal CO2 and Heart Rate Measurements","volume":"374","author":"Marmarelis","year":"2016","journal-title":"Philos. Trans. R. Soc. A Math. Phys. Eng. Sci."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1932","DOI":"10.1109\/TBME.2004.834272","article-title":"Nonlinear Modeling of the Dynamic Effects of Arterial Pressure and CO2 Variations on Cerebral Blood Flow in Healthy Humans","volume":"51","author":"Mitsis","year":"2004","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1152\/japplphysiol.00548.2005","article-title":"Cerebral Hemodynamics during Orthostatic Stress Assessed by Nonlinear Modeling","volume":"101","author":"Mitsis","year":"2006","journal-title":"J. Appl. Physiol."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"586","DOI":"10.1113\/expphysiol.2013.076455","article-title":"Assessment of Dynamic Cerebral Autoregulation and Cerebrovascular CO2 Reactivity in Ageing by Measurements of Cerebral Blood Flow and Cortical Oxygenation","volume":"99","author":"Abbink","year":"2014","journal-title":"Exp. Physiol."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"H1089","DOI":"10.1152\/ajpheart.1999.277.3.H1089","article-title":"Linear and Nonlinear Analysis of Human Dynamic Cerebral Autoregulation","volume":"277","author":"Panerai","year":"1999","journal-title":"Am. J. Physiol. Heart Circ. Physiol."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.medengphy.2003.08.001","article-title":"Neural Network Modelling of Dynamic Cerebral Autoregulation: Assessment and Comparison with Established Methods","volume":"26","author":"Panerai","year":"2004","journal-title":"Med. Eng. Phys."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"H459","DOI":"10.1152\/ajpheart.00890.2011","article-title":"Contribution of Arterial Blood Pressure and PaCO2 to the Cerebrovascular Responses to Motor Stimulation","volume":"302","author":"Panerai","year":"2012","journal-title":"Am. J. Physiol. Heart Circ. Physiol."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"2456","DOI":"10.1177\/0271678X211004131","article-title":"Cerebral Critical Closing Pressure and Resistance-Area Product: The Influence of Dynamic Cerebral Autoregulation, Age and Sex","volume":"41","author":"Panerai","year":"2021","journal-title":"J. Cereb. Blood Flow. Metab."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1007\/s10439-007-9412-9","article-title":"Multivariate System Identification for Cerebral Autoregulation","volume":"36","author":"Peng","year":"2008","journal-title":"Ann. Biomed. Eng."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Placek, M.M., Wachel, P., Iskander, D.R., Smielewski, P., Uryga, A., Mielczarek, A., Szczepa\u0144ski, T.A., and Kasprowicz, M. (2017). Applying Time-Frequency Analysis to Assess Cerebral Autoregulation during Hypercapnia. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0181851"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"H837","DOI":"10.1152\/ajpheart.00254.2016","article-title":"Identification of Human Sympathetic Neurovascular Control Using Multivariate Wavelet Decomposition Analysis","volume":"311","author":"Saleem","year":"2016","journal-title":"Am. J. Physiol. Heart Circ. Physiol."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1314","DOI":"10.1161\/HYPERTENSIONAHA.114.04236","article-title":"Relationship Between Cerebral Blood Flow and Blood Pressure in Long-Term Heart Transplant Recipients","volume":"64","author":"Smirl","year":"2014","journal-title":"Hypertension"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.mvr.2015.10.002","article-title":"Age-Related Alterations in Phase Synchronization of Oxyhemoglobin Concentration Changes in Prefrontal Tissues as Measured by near-Infrared Spectroscopy Signals","volume":"103","author":"Tan","year":"2016","journal-title":"Microvasc. Res."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1016\/j.bbr.2016.06.037","article-title":"Posture-Related Changes in Brain Functional Connectivity as Assessed by Wavelet Phase Coherence of NIRS Signals in Elderly Subjects","volume":"312","author":"Wang","year":"2016","journal-title":"Behav. Brain Res."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"H233","DOI":"10.1152\/ajpheart.1998.274.1.H233","article-title":"Transfer Function Analysis of Dynamic Cerebral Autoregulation in Humans","volume":"274","author":"Zhang","year":"1998","journal-title":"Am. J. Physiol. Heart Circ. Physiol."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"e880","DOI":"10.1097\/CCM.0000000000003966","article-title":"Feasibility of Hidden Markov Models for the Description of Time-Varying Physiologic State After Severe Traumatic Brain Injury","volume":"47","author":"Asgari","year":"2019","journal-title":"Crit. Care Med."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1349","DOI":"10.1088\/1361-6579\/aa68c4","article-title":"Cerebral Hemodynamics with Intra-Aortic Balloon Pump: Business as Usual?","volume":"38","author":"Caldas","year":"2017","journal-title":"Physiol. Meas."},{"key":"ref_57","unstructured":"Zhang, D., and Sonka, M. Classification of Diabetics with Various Degrees of Autonomic Neuropathy Based on Linear and Nonlinear Features Using Support Vector Machine. Proceedings of the Medical Biometrics."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1007\/BF01411173","article-title":"Significance of Intracranial Pressure Waveform Analysis after Head Injury","volume":"138","author":"Czosnyka","year":"1996","journal-title":"Acta Neurochir."},{"key":"ref_59","unstructured":"Hoff, J.T., Keep, R.F., Xi, G., and Hua, Y. Intracranial Pressure Monitoring: Modeling Cerebrovascular Pressure Transmission. Proceedings of the Brain Edema XIII."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Elixmann, I.M., Hansinger, J., Goffin, C., Antes, S., Radermacher, K., and Leonhardt, S. (September, January 28). Single Pulse Analysis of Intracranial Pressure for a Hydrocephalus Implant. Proceedings of the 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, San Diego, CA, USA.","DOI":"10.1109\/EMBC.2012.6346828"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"47","DOI":"10.3390\/forecast1010004","article-title":"Intracranial Pressure Forecasting in Children Using Dynamic Averaging of Time Series Data","volume":"1","author":"Farhadi","year":"2019","journal-title":"Forecasting"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"634","DOI":"10.1080\/01616412.1997.11740873","article-title":"Use of Middle Cerebral Velocity and Blood Pressure for the Analysis of Cerebral Autoregulation at Various Frequencies: The Coherence Index","volume":"19","author":"Giller","year":"1997","journal-title":"Neurol. Res."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"554","DOI":"10.1097\/CCM.0b013e3182742d0a","article-title":"Novel Methods to Predict Increased Intracranial Pressure During Intensive Care and Long-Term Neurologic Outcome after Traumatic Brain Injury: Development and Validation in a Multicenter Dataset*","volume":"41","author":"Depreitere","year":"2013","journal-title":"Crit. Care Med."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.mvr.2014.08.001","article-title":"Wavelet Coherence Analysis of Prefrontal Tissue Oxyhaemoglobin Signals as Measured Using Near-Infrared Spectroscopy in Elderly Subjects with Cerebral Infarction","volume":"95","author":"Han","year":"2014","journal-title":"Microvasc. Res."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Ang, B.-T. (2016). Intracranial Pressure and Brain Monitoring XV, Springer International Publishing. Acta Neurochirurgica Supplement.","DOI":"10.1007\/978-3-319-22533-3"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Hu, K., Lo, M.-T., Peng, C.-K., Liu, Y., and Novak, V. (2012). A Nonlinear Dynamic Approach Reveals a Long-Term Stroke Effect on Cerebral Blood Flow Regulation at Multiple Time Scales. PLoS Comput. Biol., 8.","DOI":"10.1371\/journal.pcbi.1002601"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1007\/s10558-009-9072-5","article-title":"Parametric Versus Nonparametric Transfer Function Estimation of Cerebral Autoregulation from Spontaneous Blood-Pressure Oscillations","volume":"9","author":"Jachan","year":"2009","journal-title":"Cardiovasc. Eng."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Kostoglou, K., Wright, A.D., Smirl, J.D., Bryk, K., van Donkelaar, P., and Mitsis, G.D. (2016, January 16\u201320). Dynamic Cerebral Autoregulation in Young Athletes Following Concussion. Proceedings of the 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, FL, USA.","DOI":"10.1109\/EMBC.2016.7590797"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1007\/s10877-013-9484-z","article-title":"Impaired Cerebrovascular Reactivity after Acute Traumatic Brain Injury Can Be Detected by Wavelet Phase Coherence Analysis of the Intracranial and Arterial Blood Pressure Signals","volume":"27","author":"Kvandal","year":"2013","journal-title":"J. Clin. Monit. Comput."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"2027","DOI":"10.1002\/mp.14627","article-title":"Time-Evolving Coupling Functions for Evaluating the Interaction between Cerebral Oxyhemoglobin and Arterial Blood Pressure with Hypertension","volume":"48","author":"Li","year":"2021","journal-title":"Med. Phys."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1016\/j.neuroscience.2018.01.007","article-title":"Frequency-Specific Effective Connectivity in Subjects with Cerebral Infarction as Revealed by NIRS Method","volume":"373","author":"Liu","year":"2018","journal-title":"Neuroscience"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1007\/s007010050450","article-title":"Intracranial Pressure Processing with Artificial Neural Networks: Classification of Signal Properties","volume":"142","author":"Mariak","year":"2000","journal-title":"Acta Neurochir."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Martinez-Tejada, I., Czosnyka, M., Czosnyka, Z., Juhler, M., and Smielewski, P. (2021). Causal Relationship between Slow Waves of Arterial, Intracranial Pressures and Blood Velocity in Brain. Comput. Biol. Med., 139.","DOI":"10.1016\/j.compbiomed.2021.104970"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1007\/s12028-022-01481-8","article-title":"Vector Angle Analysis of Multimodal Neuromonitoring Data for Continuous Prediction of Delayed Cerebral Ischemia","volume":"37","author":"Megjhani","year":"2022","journal-title":"Neurocrit. Care"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"024002","DOI":"10.1088\/1361-6579\/ab71f2","article-title":"Joint Time-Frequency Analysis of Dynamic Cerebral Autoregulation Using Generalized Harmonic Wavelets","volume":"41","author":"Miller","year":"2020","journal-title":"Physiol. Meas."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"1754","DOI":"10.1097\/CCM.0000000000001838","article-title":"Predicting Intracranial Pressure and Brain Tissue Oxygen Crises in Patients with Severe Traumatic Brain Injury","volume":"44","author":"Myers","year":"2016","journal-title":"Crit. Care Med."},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Naraei, P., Kenez, M., and Sadeghian, A. (2017, January 20\u201321). A Hybrid Wavelet Based K-Means Clustering Approach to Detect Intracranial Hypertension. Proceedings of the 2017 IEEE Canada International Humanitarian Technology Conference (IHTC), Toronto, ON, Canada.","DOI":"10.1109\/IHTC.2017.8058190"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1007\/BF02522477","article-title":"Frequency-Domain Analysis of Cerebral Autoregulation from Spontaneous Fluctuations in Arterial Blood Pressure","volume":"36","author":"Panerai","year":"1998","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"528","DOI":"10.3171\/2022.12.JNS221860","article-title":"Prediction of Intracranial Pressure Crises after Severe Traumatic Brain Injury Using Machine Learning Algorithms","volume":"139","author":"Petrov","year":"2023","journal-title":"J. Neurosurg."},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Pinto, H., Dias, C., and Rocha, A.P. (2022, January 11\u201315). Multiscale Information Decomposition of Long Memory Processes: Application to Plateau Waves of Intracranial Pressure. Proceedings of the 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Glasgow, UK.","DOI":"10.1109\/EMBC48229.2022.9870925"},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Porta, A., Fantinato, A., Bari, V., Gelpi, F., Cairo, B., Maria, B.D., Bertoldo, E.G., Fiolo, V., Callus, E., and Vincentiis, C.D. (2020). Evaluation of the Impact of Surgical Aortic Valve Replacement on Short-Term Cardiovascular and Cerebrovascular Controls through Spontaneous Variability Analysis. PLoS ONE, 15.","DOI":"10.1371\/journal.pone.0243869"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1152\/japplphysiol.00271.2007","article-title":"Influence of Noninvasive Peripheral Arterial Blood Pressure Measurements on Assessment of Dynamic Cerebral Autoregulation","volume":"103","author":"Sammons","year":"2007","journal-title":"J. Appl. Physiol."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"1213","DOI":"10.1109\/TBME.2017.2708609","article-title":"Robust Nonlinear Causality Analysis of Nonstationary Multivariate Physiological Time Series","volume":"65","author":"Muma","year":"2018","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_84","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_85","doi-asserted-by":"crossref","unstructured":"Depreitere, B., Meyfroidt, G., and G\u00fciza, F. (2021). Intracranial Pressure and Neuromonitoring XVII, Springer International Publishing. Acta Neurochirurgica Supplement.","DOI":"10.1007\/978-3-030-59436-7"},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Sourina, O., Ang, B.-T., and Nguyen, M.K. (2010, January 3\u20135). Fractal-Based Approach in Analysis of Intracranial Pressure (ICP) in Severe Head Injury. Proceedings of the 10th IEEE International Conference on Information Technology and Applications in Biomedicine, Corfu, Greece.","DOI":"10.1109\/ITAB.2010.5687790"},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"717","DOI":"10.1007\/BF02518874","article-title":"Neural Network Technique for Detecting Emergency States in Neurosurgical Patients","volume":"36","author":"Swiercz","year":"1998","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1007\/s007010050449","article-title":"Intracranial Pressure Processing with Artificial Neural Networks: Prediction of ICP Trends","volume":"142","author":"Swiercz","year":"2000","journal-title":"Acta Neurochir."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"971","DOI":"10.1007\/s10877-019-00392-y","article-title":"Comparison of High versus Low Frequency Cerebral Physiology for Cerebrovascular Reactivity Assessment in Traumatic Brain Injury: A Multi-Center Pilot Study","volume":"34","author":"Thelin","year":"2020","journal-title":"J. Clin. Monit. Comput."},{"key":"ref_90","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_91","unstructured":"Tsui, F.-C., Sun, M., Li, C.-C., and Sclabassi, R.J. (1995, January 20\u201323). A Wavelet Based Neural Network for Prediction of ICP Signal. Proceedings of the 17th International Conference of the Engineering in Medicine and Biology Society, Montreal, QC, Canada."},{"key":"ref_92","unstructured":"Steiger, H.-J. Generation of Very Low Frequency Cerebral Blood Flow Fluctuations in Humans. Proceedings of the Acta Neurochirurgica Supplements."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"9600","DOI":"10.1038\/s41598-022-13732-x","article-title":"Probabilistic Prediction of Increased Intracranial Pressure in Patients with Severe Traumatic Brain Injury","volume":"12","author":"Wijayatunga","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"713","DOI":"10.1089\/neu.2018.5987","article-title":"Non-Invasive Pressure Reactivity Index Using Doppler Systolic Flow Parameters: A Pilot Analysis","volume":"36","author":"Zeiler","year":"2019","journal-title":"J. Neurotrauma"},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"1306","DOI":"10.1089\/neu.2019.6726","article-title":"Statistical Cerebrovascular Reactivity Signal Properties after Secondary Decompressive Craniectomy in Traumatic Brain Injury: A CENTER-TBI Pilot Analysis","volume":"37","author":"Zeiler","year":"2020","journal-title":"J. Neurotrauma"},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"711","DOI":"10.1007\/s10877-020-00527-6","article-title":"Evaluation of the Relationship between Slow-Waves of Intracranial Pressure, Mean Arterial Pressure and Brain Tissue Oxygen in TBI: A CENTER-TBI Exploratory Analysis","volume":"35","author":"Zeiler","year":"2021","journal-title":"J. Clin. Monit. Comput."},{"key":"ref_97","unstructured":"Zhang, F., Feng, M., Pan, S.J., Loy, L.Y., Guo, W., Zhang, Z., Chin, P.L., Guan, C., King, N.K.K., and Ang, B.T. (September, January 30). Artificial Neural Network Based Intracranial Pressure Mean Forecast Algorithm for Medical Decision Support. Proceedings of the 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Boston, MA, USA."},{"key":"ref_98","unstructured":"Zhang, F., Feng, M., Loy, L.Y., Zhang, Z., and Guan, C. (2012, January 11\u201315). Online ICP Forecast for Patients with Traumatic Brain Injury. Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012), Tsukuba, Japan."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1007\/s10517-010-1095-9","article-title":"Relationship between Myogenic Reaction and Autoregulation of Cerebral Circulation","volume":"150","author":"Alexandrin","year":"2010","journal-title":"Bull. Exp. Biol. Med."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"721","DOI":"10.1152\/jappl.1979.46.4.721","article-title":"Dynamic Characteristics of Cerebral Blood Flow Response to Sinusoidal Hypoxia","volume":"46","author":"Doblar","year":"1979","journal-title":"J. Appl. Physiol."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1007\/s00210-019-01638-x","article-title":"A Simple Approach to Studying Cerebral Blood Flow during Psychological Stress","volume":"392","author":"Issam","year":"2019","journal-title":"Naunyn-Schmiedeberg\u2019s Arch. Pharmacol."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"1371","DOI":"10.1016\/j.neuroimage.2009.04.022","article-title":"A Time-Invariant Visco-Elastic Windkessel Model Relating Blood Flow and Blood Volume","volume":"47","author":"Zheng","year":"2009","journal-title":"NeuroImage"},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"1113","DOI":"10.1152\/jappl.1998.85.3.1113","article-title":"Deterioration of Cerebral Autoregulation during Orthostatic Stress: Insights from the Frequency Domain","volume":"85","author":"Zhang","year":"1998","journal-title":"J. Appl. Physiol."},{"key":"ref_104","unstructured":"Dhrymes, P.J. (1974). Econometrics: Statistical Foundations and Applications, Springer. Springer Study Edition."},{"key":"ref_105","doi-asserted-by":"crossref","unstructured":"Gu, M. (2000). Advanced Optical Imaging Theory, Springer.","DOI":"10.1007\/978-3-540-48471-4"},{"key":"ref_106","doi-asserted-by":"crossref","unstructured":"He, L., and Feng, B. (2022). Fundamentals of Measurement and Signal Analysis, Springer Nature.","DOI":"10.1007\/978-981-19-6549-4"},{"key":"ref_107","doi-asserted-by":"crossref","unstructured":"Balan, R., Begu\u00e9, M., Benedetto, J.J., Czaja, W., and Okoudjou, K.A. (2015). Excursions in Harmonic Analysis, Volume 4: The February Fourier Talks at the Norbert Wiener Center, Springer International Publishing. Applied and Numerical Harmonic Analysis.","DOI":"10.1007\/978-3-319-20188-7"},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"1014","DOI":"10.1161\/01.STR.26.6.1014","article-title":"Comparison of Static and Dynamic Cerebral Autoregulation Measurements","volume":"26","author":"Tiecks","year":"1995","journal-title":"Stroke"},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"1084","DOI":"10.1109\/29.57537","article-title":"The Use of Cone-Shaped Kernels for Generalized Time-Frequency Representations of Nonstationary Signals","volume":"38","author":"Zhao","year":"1990","journal-title":"IEEE Trans. Acoust. Speech Signal Process."},{"key":"ref_110","doi-asserted-by":"crossref","unstructured":"Proch\u00e1zka, A., Uhl\u00ed\u0159, J., Rayner, P.W.J., and Kingsbury, N.G. (1998). Signal Analysis and Prediction, Birkh\u00e4user. Applied and Numerical Harmonic Analysis.","DOI":"10.1007\/978-1-4612-1768-8"},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"2196","DOI":"10.1109\/TNNLS.2016.2581141","article-title":"Methodology of Recurrent Laguerre\u2013Volterra Network for Modeling Nonlinear Dynamic Systems","volume":"28","author":"Geng","year":"2017","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"573","DOI":"10.1007\/BF02368639","article-title":"Identification of Nonlinear Biological Systems Using Laguerre Expansions of Kernels","volume":"21","author":"Marmarelis","year":"1993","journal-title":"Ann. Biomed. Eng."},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"19617","DOI":"10.1007\/s11356-023-25148-9","article-title":"Autoregressive Models in Environmental Forecasting Time Series: A Theoretical and Application Review","volume":"30","author":"Kaur","year":"2023","journal-title":"Environ. Sci. Pollut. Res."},{"key":"ref_114","doi-asserted-by":"crossref","unstructured":"Lusia, D.A., and Ambarwati, A. (2018, January 29\u201330). Multivariate Forecasting Using Hybrid VARIMA Neural Network in JCI Case. Proceedings of the 2018 International Symposium on Advanced Intelligent Informatics (SAIN), Yogyakarta, Indonesia.","DOI":"10.1109\/SAIN.2018.8673351"},{"key":"ref_115","unstructured":"Meyers, R.A. (2003). Encyclopedia of Physical Science and Technology, Academic Press. [3rd ed.]."},{"key":"ref_116","unstructured":"Toga, A.W. (2015). Brain Mapping, Academic Press."},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"1315","DOI":"10.1038\/nbt1004-1315","article-title":"What Is a Hidden Markov Model?","volume":"22","author":"Eddy","year":"2004","journal-title":"Nat. Biotechnol."},{"key":"ref_118","unstructured":"Wu, G., Shen, D., and Sabuncu, M.R. (2016). Machine Learning and Medical Imaging, Academic Press."},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"639645","DOI":"10.3389\/fphys.2021.639645","article-title":"Vessel Enlargement in Development and Pathophysiology","volume":"12","author":"Jones","year":"2021","journal-title":"Front. Physiol."},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"1839","DOI":"10.1016\/j.ultrasmedbio.2012.05.008","article-title":"Are Hand-Held TCD Measurements Acceptable for Estimates of CBFv?","volume":"38","author":"Saeed","year":"2012","journal-title":"Ultrasound Med. Biol."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/5\/1453\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:03:56Z","timestamp":1760105036000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/5\/1453"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,23]]},"references-count":120,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2024,3]]}},"alternative-id":["s24051453"],"URL":"https:\/\/doi.org\/10.3390\/s24051453","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,23]]}}}