IP Library › Granted Patent US 12,268,479
Granted Patent B2
US 12,268,479 · App. 18/320,400 · Granted Apr 8, 2025

System and method for providing blood pressure safe zone indication during autoregulation monitoring

Inventors: Paul Stanley Addison (Edinburgh, GB); James N. Watson (Edinburgh, GB); Dean Montgomery (Edinburgh, GB)
Assignee: COVIDIEN LP
A61B5/0205A61B5/02028A61B5/021A61B5/026A61B5/14546A61B5/14553A61B5/4064A61B5/7246A61B5/742A61B5/7425A61B5/743G16H40/63G16H50/20G16H50/30A61B5/0059A61B5/0261A61B5/031
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Quick Facts
Patent No.
US 12,268,479
App. No.
18/320,400
Granted
Apr 8, 2025
Kind
B2
Abstract

A method for monitoring autoregulation includes, using a processor, using a processor to execute one or more routines on a memory. The one or more routines include receiving one or more physiological signals from a patient, determining a correlation-based measure indicative of the patient's autoregulation based on the one or more physiological signals, and generating an autoregulation profile of the patient based on autoregulation index values of the correlation-based measure. The autoregulation profile includes the autoregulation index values sorted into bins corresponding to different blood pressure ranges. The one or more routines also include designating a blood pressure range encompassing one or more of the bins as a blood pressure safe zone indicative of intact regulation and providing a signal to a display to display the autoregulation profile and a first indicator of the blood pressure safe zone.

Claims (61)

1. A monitor for monitoring autoregulation, the monitor comprising:

a display;

a memory encoding one or more processor-executable instructions; and

one or more processors configured to access and execute the one or more processor-executable instructions encoded by the memory, wherein the one or more processor-executable instructions, when executed, cause the one or more processors to:

receive one or more physiological signals from a patient;

determine a correlation-based measure indicative of an autoregulation of the patient based on the one or more physiological signals;

generate an autoregulation profile of the patient based on autoregulation index values of the correlation-based measure, wherein the autoregulation profile comprises the autoregulation index values sorted into bins corresponding to different blood pressure ranges;

provide a first signal to the display or another output device to provide an indicator of a distance of a current blood pressure from a target blood pressure; and

provide a second signal to the display to display the autoregulation profile.

2. The monitor of claim 1 , wherein the indicator of the distance of the current blood pressure from the target blood pressure comprises a first indicator,

wherein the one or more processor-executable instructions, when executed, cause the one or more processors to:

designate a blood pressure range encompassing one or more of the bins as a blood pressure safe zone indicative of intact autoregulation; and

provide the second signal to the display to display a second indicator of the blood pressure safe zone.

3. The monitor of claim 1 , wherein the indicator of the distance of the current blood pressure from the target blood pressure is a visual or audible indicator.

4. The monitor of claim 1 , wherein the one or more processor-executable instructions, when executed, cause the one or more processors to:

change a color of the indicator displayed on the display based on the distance of the current blood pressure of the patient from the target blood pressure.

5. The monitor of claim 4 , wherein the color of the indicator changes between green, amber, and red.

6. The monitor of claim 1 , wherein the indicator is a blip bar on the display, and wherein the one or more processor-executable instructions, when executed, cause the one or more processors to:

change a level of the blip bar based on the distance of the current blood pressure of the patient from the target blood pressure.

7. The monitor of claim 1 , wherein the correlation-based measure is one of a cerebral oximetry index, a hemoglobin volume index, or a mean velocity index.

8. The monitor of claim 1 , wherein the one or more processor-executable instructions, when executed, cause the one or more processors to receive a user input of the target blood pressure.

9. The monitor of claim 2 , wherein the one or more physiological signals comprise a regional oxygen saturation signal and a blood pressure signal,

wherein the one or more processor-executable instructions, when executed, cause the one or more processors to:

generate a curve from the regional oxygen saturation signal and the blood pressure signal to generate the autoregulation profile;

determine both a lower limit (LLA) and an upper limit (ULA) of autoregulation from the curve to designate the blood pressure safe zone; and

determine the target blood pressure based on the curve.

10. The monitor of claim 9 , wherein the one or more processor-executable instructions, when executed, cause the one or more processors to designate a point equidistant between the LLA and ULA as the target blood pressure.

11. The monitor of claim 9 , wherein the one or more processor-executable instructions, when executed, cause the one or more processors to:

determine a first gradient of the curve at a first blood pressure portion of the curve lower than the LLA;

determine a second gradient of the curve at a second blood pressure portion of the curve higher than the ULA; and

determine the target blood pressure based on the first gradient, the second gradient, or a combination thereof.

12. The monitor of claim 2 , wherein the one or more processor-executable instructions, when executed, cause the one or more processors to provide the second signal to the display to display a blood pressure history of the patient, wherein the blood pressure history depicts an amount of time blood pressure measurements within the bins are within the blood pressure safe zone.

13. The monitor of claim 1 , wherein the one or more processor-executable instructions, when executed, cause the one or more processors to:

receive patient specific inputs, the patient specific inputs being physical characteristics of the patient;

designate a blood pressure range encompassing one or more of the bins as an initial blood pressure safe zone indicative of intact autoregulation utilizing at least historical population data based on the patient specific inputs; and

provide the second signal to the display to display a second indicator of the initial blood pressure safe zone.

14. The monitor of claim 13 , wherein the one or more processor-executable instructions, when executed, cause the one or more processors to:

determine if there is sufficient data within the autoregulation profile to designate a patient-specific blood pressure safe zone;

in response to determining that there is sufficient data, designate the blood pressure range encompassing one or more of the bins as a patient-specific blood pressure safe zone indicative of intact autoregulation; and

provide a third signal to the display to display a respective indicator of the patient-specific blood pressure safe zone,

wherein the second indicator of the initial blood pressure safe zone comprises a first color, and wherein the respective indicator of the patient-specific blood pressure safe zone comprises a second color different from the first color.

15. The monitor of claim 2 , wherein the one or more processor-executable instructions, when executed, cause the one or more processors to provide a third signal to the display or other output device to provide a third indication that the current blood pressure of the patient is outside the blood pressure safe zone.

16. A method for monitoring autoregulation, the method comprising:

receiving, by one or more processors executing one or more instructions encoded on a memory, one or more physiological signals from a patient;

determining, by the one or more processors, a correlation-based measure indicative of an autoregulation of the patient based on the one or more physiological signals;

generating, by the one or more processors, an autoregulation profile of the patient based on autoregulation index values of the correlation-based measure, wherein the autoregulation profile comprises the autoregulation index values sorted into bins corresponding to different blood pressure ranges;

providing, by the one or more processors and to a display or other output device, a first signal to provide an indicator of a distance of a current blood pressure from a target blood pressure; and

providing, by the one or more processors and to a display, a second signal to display the autoregulation profile.

17. The method of claim 16 , further comprising:

designating, by the one or more processors, a blood pressure range encompassing one or more of the bins as a blood pressure safe zone indicative of intact autoregulation,

wherein the indicator of the distance of the current blood pressure from the target blood pressure comprises a first indicator, and

wherein providing the second signal further comprises providing the second signal to display a second indicator of the blood pressure safe zone.

18. The method of claim 16 , wherein the indicator of the distance of the current blood pressure from the target blood pressure is a visual or audible indicator.

19. The method of claim 16 , further comprising:

changing, by the one or more processors, a color of the indicator displayed on the display based on the distance of the current blood pressure of the patient from the target blood pressure.

20. A device comprising a computer-readable medium having executable instructions stored thereon, configured to be executable by processing circuitry for causing the processing circuitry to:

receive one or more physiological signals from a patient;

determine a correlation-based measure indicative of an autoregulation of the patient based on the one or more physiological signals;

generate an autoregulation profile of the patient based on autoregulation index values of the correlation-based measure, wherein the autoregulation profile comprises the autoregulation index values sorted into bins corresponding to different blood pressure ranges;

provide a first signal to a display or another output device to provide an indicator of a distance of a current blood pressure from a target blood pressure; and

provide a second signal to the display to display the autoregulation profile.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded May 19, 2023
From: ADDISON, PAUL STANLEY; WATSON, JAMES N.; MONTGOMERY, DEAN
To: COVIDIEN LP
Reel/Frame 063700/0141 →
Continuity (4)
Continuation 16675394 · Nov 6, 2019
Continuation 15296150 · Oct 18, 2016
Provisional Application 62243341 · Oct 19, 2015
Related Publication 20230284914A1 · Sep 14, 2023
References Cited (154)
US 4776339A · Schreiber · 1988 [cited by applicant]
US 5351685A · Potratz · 1994 [cited by applicant]
US 5482034A · Lewis et al. · 1996 [cited by applicant]
US 5533507A · Potratz · 1996 [cited by applicant]
US 5577500A · Potratz · 1996 [cited by applicant]
US 5584296A · Cui et al. · 1996 [cited by applicant]
US 5626140A · Feldman et al. · 1997 [cited by applicant]
US 5803910A · Potratz · 1998 [cited by applicant]
US 5934277A · Mortz · 1999 [cited by applicant]
US 6385471B1 · Mortz · 2002 [cited by applicant]
US 6438399B1 · Kurth · 2002 [cited by applicant]
US 6453183B1 · Walker · 2002 [cited by applicant]
US 6505060B1 · Norris · 2003 [cited by applicant]
US 6510329B2 · Heckel · 2003 [cited by applicant]
US 6599251B2 · Chen et al. · 2003 [cited by applicant]
US 6668182B2 · Hubelbank · 2003 [cited by applicant]
US 6714803B1 · Mortz · 2004 [cited by applicant]
US 6754516B2 · Mannheimer · 2004 [cited by applicant]
US 6896661B2 · Dekker · 2005 [cited by applicant]
US 6987994B1 · Mortz · 2006 [cited by applicant]
US 7001337B2 · Dekker · 2006 [cited by applicant]
US 7221969B2 · Stoddart et al. · 2007 [cited by applicant]
US 7268873B2 · Sevick-Muraca et al. · 2007 [cited by applicant]
US 7744541B2 · Baruch et al. · 2010 [cited by applicant]
US 8556811B2 · Brady · 2013 [cited by applicant]
US 10219705B2 · Addison et al. · 2019 [cited by applicant]
US 10499818B2 · Addison et al. · 2019 [cited by applicant]
US 10667733B2 · Simpson et al. · 2020 [cited by applicant]
US 20040097797A1 · Porges et al. · 2004 [cited by applicant]
US 20050004479A1 · Townsend et al. · 2005 [cited by applicant]
US 20050033129A1 · Edgar, Jr. et al. · 2005 [cited by applicant]
US 20050192488A1 · Bryenton et al. · 2005 [cited by applicant]
US 20050192493A1 · Wuori · 2005 [cited by applicant]
US 20070004977A1 · Norris · 2007 [cited by applicant]
US 20070049812A1 · Aoyagi et al. · 2007 [cited by applicant]
US 20080081974A1 · Pav · 2008 [cited by applicant]
US 20080146901A1 · Katura et al. · 2008 [cited by applicant]
US 20080200785A1 · Fortin · 2008 [cited by applicant]
US 20080228053A1 · Wang et al. · 2008 [cited by applicant]
US 20090326386A1 · Sethi et al. · 2009 [cited by applicant]
US 20100010322A1 · Brady · 2010 [cited by applicant]
US 20100030054A1 · Baruch et al. · 2010 [cited by applicant]
US 20100049082A1 · Hu et al. · 2010 [cited by applicant]
US 20100063405A1 · Kashif et al. · 2010 [cited by applicant]
US 20110046459A1 · Zhang et al. · 2011 [cited by applicant]
US 20110077490A1 · Simpson et al. · 2011 [cited by applicant]
US 20110105912A1 · Widman · 2011 [cited by examiner]
US 20120149994A1 · Luczyk et al. · 2012 [cited by applicant]
US 20120253211A1 · Brady et al. · 2012 [cited by applicant]
US 20120271130A1 · Benni · 2012 [cited by applicant]
US 20130190632A1 · Baruch et al. · 2013 [cited by applicant]
US 20140073888A1 · Sethi · 2014 [cited by applicant]
US 20140275818A1 · Kassem et al. · 2014 [cited by applicant]
US 20140278285A1 · Marmarelis et al. · 2014 [cited by applicant]
US 20160106372A1 · Addison · 2016 [cited by applicant]
US 20160324425A1 · Addison et al. · 2016 [cited by applicant]
US 20160345913A1 · Montgomery et al. · 2016 [cited by applicant]
US 20160367197A1 · Addison et al. · 2016 [cited by applicant]
US 20170000395A1 · Addison et al. · 2017 [cited by applicant]
US 20170000423A1 · Addison et al. · 2017 [cited by applicant]
US 20170095161A1 · Addison et al. · 2017 [cited by applicant]
US 20170105631A1 · Addison et al. · 2017 [cited by applicant]
US 20170105672A1 · Addison et al. · 2017 [cited by applicant]
US 20170327779A1 · Horiguchi et al. · 2017 [cited by applicant]
US 20180014791A1 · Montgomery et al. · 2018 [cited by applicant]
US 20180049649A1 · Addison et al. · 2018 [cited by applicant]
US 20200069195A1 · Addison et al. · 2020 [cited by applicant]
WO 9843071A1 · 1998 [cited by applicant]
WO 0059374 · 2000 [cited by applicant]
WO 03000125A1 · 2003 [cited by applicant]
WO 03071928A2 · 2003 [cited by applicant]
WO 2004075746A2 · 2004 [cited by applicant]
WO 2008097411A1 · 2008 [cited by applicant]
WO 2016182853A1 · 2016 [cited by applicant]
Addison, P. S., et al.; “Low-Oscillation Complex Wavelets,” Journal of Sound and Vibration, Jul. 2002, vol. 254, Elsevier Science Ltd., pp. 1-30. [cited by applicant]
Addison, P. S.; “The Illustrated Wavelet Transform Handbook,” 2002, 1OP Publishing Ltd., Bristol, UK, Ch. 2. (Applicant points out, in accordance with MPEP 609.04(a), that the year of publication, 2002, is sufficiently … [cited by applicant]
Addison, Paul 5., et al.; “A novel time-frequency-based 3D Lissajous figure method and its application to the determination of oxygen saturation from the photoplethysmogram,” Institute of Physic Publishing, Meas. Sci. T… [cited by applicant]
Barreto, Armando B., et al.; “Adaptive LMS Delay Measurement in dual Blood Volume Pulse Signals for Non-Invasive Monitoring, ” IEEE, pp. 117-120 Apr. 1997. [cited by applicant]
Bassan, Haim, et al.; “Identification of pressure passive cerebral perfusion and its mediators after infant cardiac surgery,” Pediatric Research Foundation, vol. 57, No. 1, 2005; pp. 35-41. [cited by applicant]
Belal, Suliman Yousef, et al.; “A fuzzy system for detecting distorted plethysmogram pulses in neonates and paediatric patients,” Physiol. Meas., vol. 22, pp. 397-412 {2001). (Applicant points out, in accordance with MP… [cited by applicant]
Brady, Ken M., et al.; “Continuous Measurement of Autoregulation by Spontaneous Fluctuations in Cerebral Perfusion Pressure Comparison of 3 Methods,” NIH Public Access Author Manuscript, Stroke, 2008, 39(9), pp. 1-13. [cited by applicant]
Brady, Ken M., et al.; “Continuous time-domain analysis of cerebrovascular autoregulation using near-infrared spectroscopy,” American Stroke Association, DOI: 10.116llstrokeaha.107.485706, Aug. 2007, pp. 2818-2825. [cited by applicant]
Brady, Ken M., et al.; “Monitoring cerebral blood flow pressure autoregulation in pediatric patients during cardiac surgery,” Stroke 2010;41: 1957-1962 (http://stroke.ahajournals.org/content/41/9/1957 .full). [cited by applicant]
Brady, Ken M., et al.; “Noninvasive Autoregulation Monitoring with and without Intracranial Pressure in a Naive Piglet Brain,” Neuroscience in Anesthesiology and Perioperative Medicine, 2010, vol. 111, No. 1, Internatio… [cited by applicant]
Brady, Kenneth, et al.; “Real-Time Continuous Monitoring of Cerebral Blood Flow Autoregulation Using Near-Infrared Spectroscopy in Patients Undergoing Cardiopulmonary Bypass,” Stroke, 2010, 41, American Heart Associatio… [cited by applicant]
Caicedo, Alexander, et al.; “Cerebral Tissue Oxygenation and Regional Oxygen Saturation Can be Used to study Cerebral Autoregulation in Prematurely Born Infants,” Pediatric Research, vol. 69, No. 6, Jun. 1, 2011, pp. 54… [cited by applicant]
Caicedo, Alexander, et al.; “Detection of cerebral autoregulation by near-infrared spectroscopy in neonates: performance analysis of measurement methods,” Journal of Biomedical Optics 17 (11) pp. 117003-1-117003-9 (Nov.… [cited by applicant]
Chan, K.W., et al.; “17 .3: Adaptive Reduction of Motion Artifact from Photoplethysmographic Recordings using a Variable Step-Size LMS Filter,” IEEE, pp. 1343-1346 (2002)+A10. [cited by applicant]
Chen, Li, et al.; “The role of pulse oximetry plethysmographic waveform monitoring as a marker of restoration of spontaneous circulation: a pilot study,” Chin Crit Care Med, 2015, vol. 27, No. 3, pp. 203-208. [cited by applicant]
Chen, Liangyou, et al.; “IS respiration-induced variation in the photoplethysmogram associated with major hypovolemia inpatients with actue tramatic injuries,” Shock, vol. 34, No. 5, pp. 455-460 (2010). [cited by applicant]
Cheng, Ran, et al.; “Noninvasive optical evaluation of spontaneous low frequency oscillations in cerebral hemodynamics”, Neuroimage, Academic Press, vol. 62, No. 3, May 24, 2012, pp. 1445-1454. [cited by applicant]
Chuan et al., “Is cerebrovascular autoregulation associated with outcomes after major noncardiac surgery? A prospective observational pilot study,” Acta Anaesthesiol Scand., Aug. 5, 2018, 10 pp. [cited by applicant]
Coetzee, Frans M.; “Noise-Resistant Pulse Oximetry Using a Synthetic Reference Signal,” IEEE Transactions on Biomedical Engineering, vol. 47, No. 8, Aug. 2000, pp. 1018-1026. [cited by applicant]
Cyrill, D., et al.; “Adaptive Comb Filter for Quasi-Periodic Physiologic Signals,” Proceedings of the 25th Annual International Conference of the IEEE EMBS, Cancun, Mexico, Sep. 17-21, 2003;pp. 2439-2442. [cited by applicant]
Cysewska-Sobusaik, Anna; “Metrological Problems With noninvasive Transillumination of Living Tissues,” Proceedings of SPIE, vol. 4515, pp. 15-24, Jul. 2001. [cited by applicant]
Czosnyka, Marek, et al.; “Monitoring of cerebrovascular autoregulation: Facts, Myths, and Missing links,” Neurocrit Care, Jun. 2009, 10:373-386. [cited by applicant]
Daubechies, Ingrid, et al.; “A Nonlinear Squeezing of the Continuous Wavelet Transform Based on Auditory Nerve Models,” Princeton University, 1996, Acoustic Processing Department, NY, pp. iii, 1-17. (Applicant points ou… [cited by applicant]
Daubechies, Ingrid, et al.; “Synchrosqueezed Wavelet Transforms: an Empirical Mode Decomposition-like Tool,” Princeton University, Aug. 2010, Applied and Computational Harmonic Analysis, pp. 1-32. [cited by applicant]
Dias, Celeste, et al.; “Optimal Cerebral Perfusion Pressure Management at Bedside: A Single-Center Pilot Study,” Neurocritical care, vol. 23, No. 1, Jan. 8, 2015; pp. 92-102; ISSN: 1541-6933. [cited by applicant]
East, Christine E., et al.; “Fetal Oxygen Saturation and Uterine Contractions During Labor,” American Journal ofPerinatology, vol. 15, No. 6, pp. 345-349 (Jun. 1998). [cited by applicant]
Edrich, Thomas, et al.; “Can the Blood Content of the Tissues be Determined Optically During Pulse Oximetry Without Knowledge of the Oxygen Saturation?—An In-Vitro Investigation,” Proceedings of the 20th Annual Internat… [cited by applicant]
Eichhorn, Lars, et al.; “Evaluation of newar-infrared spectroscopy under apnea-dependent hypoxia in humans,” Journal of Clinical Monitoring and Computing, vol. 29, No. 6, Feb. 4, 2015, pp. 749-757. [cited by applicant]
Examination Report from counterpart European Application No. 16790801.1, dated Dec. 5, 2019, 4 pp. [cited by applicant]
Gao, Yuanjuin, et al.; “Response of cerebral tissue oxygenation and arterial blood pressure to postural change assessed by wavelet phase coherence analysis”, 2014 7th International conference on Biomedical Engineering a… [cited by applicant]
Ge, Z.; “Significance tests for the wavelet cross spectrum and wavelet linear coherence,” Annales Geophysicae, Dec. 2, 2008, 26, Copernicus Publications on behalf of European Geosciences Union, pp. 3819-3829. [cited by applicant]
Gesquiere, Michael J., et al., “Impact of withdrawal of 450 ML of blood on respiration-induced oscillations of the ear plethysmographic waveform,” Journal of Clinical Monitoring and Computing Aug. 2007, 21:277-282. [cited by applicant]
Goldman, Julian M.; “Masimo Signal Extraction Pulse Oximetry,” Journal of Clinical Monitoring and Computing, vol. 16, pp. 475-483, Sep. 2000. [cited by applicant]
Gommer, Erik D., et al.; “Dynamic cerebral autoregulation: different signal processing methods without influence on results and reproducibility”; Medical & Biological Engineering & Computer; vol. 48, No. 12, Nov. 4, 201… [cited by applicant]
Hamilton, Patrick S., et al.; “Effect of Adaptive Motion-Artifact Reduction on QRS Detection,” Biomedical Instrumentation & Technology, pp. 197-202 (May-Jun. 2000). [cited by applicant]
Huang, J., et al.; “Low Power Motion Tolerant Pulse Oximetry,” Anesthesia & Analgesia 2002 94: S103. (Applicant points out, in accordance with MPEP 609.04(a), that the year of publication, 2002, is sufficiently earlier … [cited by applicant]
International Preliminary Report on Patentability from International Application No. PCT/US2016/057443, mailed Apr. 24, 2018, 8 pp. [cited by applicant]
International Search Report and Written Opinion from PCT Application No. PCT/US2016/057443 dated Jan. 24, 2017, 15 pp. [cited by applicant]
Johansson, A.; “Neural network for photoplethysmographic respiratory rate monitoring,” Medical & Biological Engineering & Computing, vol. 41, pp. 242-248 (2003). [cited by applicant]
Kaestle, S.; “Determining Artefact Sensitivity ofNew Pulse Oximeters in Laboratory Using Signals Obtained from Patient,” Biomedizinische Technik, vol. 45 (2000). [cited by applicant]
Kim, J.M., et al.; Signal Processing Using Fourier & Wavelet Transform for pulse oximetry/' pp. II-310-11-311, Jul. 2001. [cited by applicant]
Kirkham, S.K., et al.; “A new mathematical model of dynamic cerebral autoregulation based on a flow dependent feedback mechanism; Dynamic cerebral autoregulation modelling,” Physiological Measurement, Institute of Physi… [cited by applicant]
Leahy, Martin J., et al.; “Sensor Validation in Biomedical Applications,” IFAC Modelling and Control in Biomedical Systems, Warwick, UK; pp. 221-226, Dec. 1997. [cited by applicant]
Lee, C.M., et al.; “Reduction of motion artifacts from photoplethysmographic recordings using wavelet denoising approach,” IEEE EMBS Asian-Pacific Conference on Biomedical Engineering, Oct. 20-22, 2003; pp. 194-195. [cited by applicant]
Lee, Jennifer K., et al.; A pilot study of cerebrovascular reactivity autoregulation after pediatric cardiac arrest; Resuscitation 85, Oct. 2014, Elsevier Ireland Ltd., pp. 1387-1393. [cited by applicant]
Maletras, Francais-Xavier, et al.; “Construction and calibration of a new design of Fiber Optic Respiratory Plethysmograph (FORP),” Optomechanical Design and Engineering, Proceedings of SPIE, vol. 4444, pp. 285-293, Nov… [cited by applicant]
Massart, Desire L., et al.; “Least Median of Squares: A Robust Method for Outlier and Model Error Detection in Regression and Calibration,” Analytica Chimica Acta, Jan. 13, 1986, Elsevier Science Publishers B.V., The Ne… [cited by applicant]
McGrath, S.P., et al.; “Pulse oximeter plethysmographic waveform changes in awake, spontaneously breathing, hypovolemic volunteers,” Anesth. Analg. vol. 112 No. 2, pp. 368-374, epublished Jan. 26, 2010. [cited by applicant]
Montgomery, Dean, et al.; “Data clustering methods for the determination of cerebral autoregulation functionality,” Journal of Clinical Monitoring and Computing, vol. 3 0, No. 5, Sep. 16, 2015, pp. 661-668. [cited by applicant]
Morren, G., et al.; “Detection of autoregulation in the brain of premature infants using a novel subspace-based technique,” 23rd Annual International Conference of IEEE Engineering in Medicine and Biology Society, Oct. … [cited by applicant]
Morren, Geert, et al.; “Quantitation of the concordance between cerebral intravascular oxygenation and mean arterial blood pressure for the detection of impaired autoregulation,” 29th Annual Meeting of the International… [cited by applicant]
Neumann, R., et al.; “Fourier Artifact suppression Technology Provides Reliable Sp02,” Anesthesia & Analgesia 2002, 94: S105. (Applicant points out, in accordance with MPEP 609.04(a), that the year of publication, 2002,… [cited by applicant]
Obrig, Hellmuth, et al.; “Spontaneous low frequency oscillations of cerebral hemodynamics and metabolism in human adults,” Neuroimage 12, 623-639, Dec. 2000. [cited by applicant]
Odagiri, Y.; “Pulse Wave Measuring Device,” Micromechatronics, vol. 42, No. 3, pp. 6-11 (published Sep. 1998) (Article in Japanese—contains English summary of article). [cited by applicant]
Office Action in the Chinese language from counterpart Chinese Application No. 201680059967.4, dated Apr. 15, 2020, 8 pp. [cited by applicant]
Ono, Masahiro, et al.; “Validation of a stand-alone near-infrared spectroscopy system for monitoring cerebral autoregulaiton during cardiac surgery,” International Anethesia Research Society, Jan. 2013, vol. 116, No. 1,… [cited by applicant]
Panerai, B.; “Cerebral Autoregulation: from models to clinical Applications,” Cardiovascular Engineering: an International Journal, vol. 8, No. 1, Nov. 28, 2007, (28 pgs.). [cited by applicant]
Payne, Stephen J., et al.; “Tissue Oxygenation Index as a Measure of Cerebral Autoregulation,” Biomedial Engineering, Feb. 2004, Innsbmck, Austria, pp. 546-550. [cited by applicant]
Prosecution History from U.S. Appl. No. 15/296,150, dated Aug. 1, 2018 through Aug. 8, 2019,41 pp. [cited by applicant]
Prosecution History from U.S. Appl. No. 16/675,394, dated Sep. 15, 2022 through Jan. 17, 2023, 29 pp. [cited by applicant]
Reinhard, Matthias, et al.; “Oscillatory cerebral hemodynamics—the macro-vs. microvascular level,” Journal of the Neurological Sciences 250, Dec. 2006, 103-109. [cited by applicant]
Reinhard, Matthias, et al.; “Spatial mapping of dynamic cerebral autoregulation by multichannel near-infrared spectroscopy in high-grade carotid artery disease”, International Society for optical Engineering, SPIE, vol.… [cited by applicant]
Relente, A.R., et al.; “Characterization and Adaptive Filtering of Motion Artifacts in Pulse Oximetry using Accelerometers,” Proceedings of the Second JOint EMBSIBMES Conference, Houston, Texas, Oct. 23-26, 2002; pp. 17… [cited by applicant]
Response to Extended Search Report dated Dec. 5, 2019, from counterpart European Application No. 16790801.1, filed Apr. 15, 2020, 15 pp. [cited by applicant]
Rowley, A.B., et al.; “Synchronization between arterial blood pressure and cerebral oxyhaemoglobin concentration investigated by wavelet cross-correlation,” Physiol. Meas., vol. 28, No. 2, Feb. 2007, pp. 161-173. [cited by applicant]
Shamir, M., et al.; “Pulse oximetry plethysmographic waveform during changes in blood volume,” British Journal of Anaesthesia 82(2): 178-81, Feb. 1999. [cited by applicant]
Sorensen, Henrik, et al.; “A note on arterial to venous oxygen saturation as reference for NIRS determined frontal lobe oxygen saturation in healthy humans,” Frontiers in Physiology, vol. 4, Jan. 2014, pp. 1-3. [cited by applicant]
Stetson, Paul F.; “Determining Heart Rate from Noisey Pulse Oximeter Signals Using Fuzzy Logic,” The IEEE International Conference on Fuzzy Systems, St. Louis, Missouri, May 25-28, 2003; pp. 1053-1058. [cited by applicant]
Such, Hans Olaf; “Optoelectronic Non-invasive Vascular Diagnostics Using Multiple Wavelength and Imaging Approach,” Dissertation, (1998). (Applicant points out, in accordance with MPEP 609.04(a), that the year of public… [cited by applicant]
Todd, Bryan, et al.; The Identification of Peaks in Physiological Signals, Computers and Biomedical Research, vol. 32, pp. 322-335, Aug. 1999. [cited by applicant]
Tsuji, Miles, et al.; “Cerebral intravascular oxygenation correlates with mean arterial pressure in critically ill premature infants,” American Academy of Pediatrics, Oct. 2000; 106; pp. 625-632. [cited by applicant]
Wagner, Bendicht P., et al.; “Dynamic cerebral autoregulatory response to blood pressure rise measured by near-infrared spectroscopy and intracranial pressure,” Critical Care Medicine, Sep. 2002, vol. 30, No. 9, pp. 201… [cited by applicant]
Whitaker, E., et al.; “Cerebrovascular Autoregulation After Pediatric Cardiac Arrest,” Neuro-85, 2012, 2 pgs. (Applicant points out, in accordance with MPEP 609.04(a), that the year of publication, 2012, is sufficiently… [cited by applicant]
Williams, Monica, et al.; “Intraoperative blood pressure and Cerebral perfusion: strategies to clarify hemodynamic goals,” Paediatric Anaesthesia, vol. 24, No. 7, Jul. 12, 2014; pp. 657-667; XP055331904. [cited by applicant]
Wong, Flora Y., et al.; “Impaired Autoregulation in preterm infants identified by using spatially resolved spectroscopy,” American Academy of Pediatrics DO 1: 10.1542, Mar. 2008, e604-611. [cited by applicant]
Wu, Dongmei, et al.; “Na /H Exchange inhibition delays the onset of hypovolemic circulatory shock in pigs,” Shock, vol. 29, No. 4, pp. 519-525 {2008). (Applicant points out, in accordance with MPEP 609.04(a), that the y… [cited by applicant]
Wu, et al.; “Using synchrosqueezing transform to discover breathing dynamics from ECG signals,” arXiv:1105.1571, vol. 2, Dec. 2013, pp. 1-9. [cited by applicant]
Wu, Hau-tieng, et al.; “Evaluating physiological dynamics via Synchrosqueezing: Prediction of Ventilator Weaning,” Journal of Latex Class Files, vol. II, No. 4, Dec. 2012, pp. 1-9. [cited by applicant]
Zhang, Rong, et al.; “Transfer function analysis of dynamic cerebral autoregulation in humans,” 1998 the American Physiological Society; pp. H233-H241. [cited by applicant]
Zweifel, Christian, et al.; “Continuous time-domain monitoring of cerebral autoregulation in neurocritical care,” Medical Engineering & Physics, Elsevier Ltd., vol. 36, No. 5, 2014, pp. 638-645. [cited by applicant]