Aleksandar Mihaylov MBP 3302 Supervisors Ken Tichauer Keith

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Aleksandar Mihaylov MBP 3302 Supervisors: Ken Tichauer Keith St. Lawrence Near-Infrared Spiroximetry

Aleksandar Mihaylov MBP 3302 Supervisors: Ken Tichauer Keith St. Lawrence Near-Infrared Spiroximetry

Overview �NIRS What is it? Why use it? �NIRS Methodology Data acquisition Fitting Filtering

Overview �NIRS What is it? Why use it? �NIRS Methodology Data acquisition Fitting Filtering and Spectral analysis �Results �Conclusion

What is NIRS? �Objective Measure blood oxygenation using near-infrared light Non-invasive methodology Real time

What is NIRS? �Objective Measure blood oxygenation using near-infrared light Non-invasive methodology Real time monitoring �Applications Muscle metabolism Vascular disorder studies Functional brain imaging

Applications cont’d METABOLISM http: //www. daviddarling. info/encyclopedia/P/pulse_oximetry. html BRAIN FUNCTION http: //www. rtmagazine. com/issues/articles/2007

Applications cont’d METABOLISM http: //www. daviddarling. info/encyclopedia/P/pulse_oximetry. html BRAIN FUNCTION http: //www. rtmagazine. com/issues/articles/2007 -10_07. asp

Why use NIRS? �Other Methods Blood gas analysis Oxygen sensors �NIRS benefits Non-invasive Allows

Why use NIRS? �Other Methods Blood gas analysis Oxygen sensors �NIRS benefits Non-invasive Allows for real time monitoring Easy to implement

Methodology - Data Acquisition Near-Infrared light Wavelengths of 600 -900 nm Deep penetration Highly

Methodology - Data Acquisition Near-Infrared light Wavelengths of 600 -900 nm Deep penetration Highly sensitive to Hb saturation Probe Layout Discrete vs. Broadband Transmission vs. Reflectance Positioning Relative measure of volume change http: //www. pages. drexel. edu/~kmg 462/

Methodology - Fitting Franceschini, et al, 2002 � Separate oxy from non-oxy haemoglobin data

Methodology - Fitting Franceschini, et al, 2002 � Separate oxy from non-oxy haemoglobin data � Deep penetration – one data set for arterial, venous and capillary compartments.

Methodology – Spectral Analysis �Pulsatile nature of blood vessels Arterial – pulsations at the

Methodology – Spectral Analysis �Pulsatile nature of blood vessels Arterial – pulsations at the heart rate Venous – pulsations at the respiratory frequency �Fourier Domain analysis Further separation into compartments Power in spectrum relative to concentration of Hb. O 2 or Hb

Results

Results

Results cont’d �Accuracy (Unpublished Data) Run# 1: Sv. O 2[%] 2: Sv. O 2[%]

Results cont’d �Accuracy (Unpublished Data) Run# 1: Sv. O 2[%] 2: Sv. O 2[%] 3: Sv. O 2[%] Expected 87. 5 67. 25 26 Calculated 74. 9 47. 1 31. 1 Error 14. 4 29. 9 -19. 6 �Improvements Higher sensitivity – allow for low Sv. O 2 measurements Improved fitting algorithm Artifact Correction

Conclusion �NIRS methodology Non-invasive Easy to implement Real-time monitoring �Further work Artifact correction Probe

Conclusion �NIRS methodology Non-invasive Easy to implement Real-time monitoring �Further work Artifact correction Probe sensitivity and bandwidth Increased accuracy

References � Maria Angela Franceschini, et al, Near-infrared spiroximetry: noninvasive measurements of venous saturation

References � Maria Angela Franceschini, et al, Near-infrared spiroximetry: noninvasive measurements of venous saturation in piglets and human subjects, J Appl Physiol 92: 372 -384, 2002. � B. L. Horecker, The absorption spectra of hemoglobin and its derivatieves in the visible and near infra-red regions, ASBMB, 1942 � Willem G. Zijlstra, Anneke Buursma, O. W. van Assendelft, Visible and near infrared absorption spectra of human and animal haemoglobin: determination and application, VSP 2000 � Kenneth M. Tichauer, Derek W. Brown, Jennifer Hadway, Ting-Yim Lee, Keith St. Lawrence, Near-infrared spectroscopy measurements of cerebral blood flow and oxygen consumption following hypoxia-ischemia in newborn piglets, J Appl Physiol 2006