1. Kara Bliley Gina Lee Allison Powers Advisor: Tina V. Hartert, M.D., M.P.H. Quantification of Respiratory Waveform Variations in Pulse Oximetry Tracings

2. Background Readings are based on pulsatile absorption Arterial blood assumed to be the only pulsatile absorbance between light source and photodetector Pulse oximetry:

3. Background Pulse oximetry is the measure of “functional O 2 saturation” which is defined as the percentage of oxyhemoglobin (O 2 Hb) relative to the total amount of Hb available for binding:

4. Background Pulsus Paradoxus: –Defined as an abnormally large decline in systemic arterial pressure during inspiration (>10mmHg) –Observed in severe asthma, heart failure, and forced respiratory effort

5. Background How is pulsus paradoxus found? Normal Tracing Tracing Exemplifying Pulsus Paradoxus Waveform allows for recognition of pulsus paradoxus Pulsus paradoxus is normally determined manually using a blood pressure cuff time Functional O2 saturation

6. Objective To develop an algorithm to quantify pulsus paradoxus Normal arterial pressure trace:Pulsus paradoxus: Functional O2 saturation time

7. Methods: Digitizing Data Entered the data using a digitizer About six complete cycles of data entered for each tracing Number of data sets: 3 normal, 7 abnormal

8. MATLAB code load data2.txt; l2 = length(data2); [time2 pulse2] = textread('data2.txt', '%f %f',l2); N2 = length(pulse2); t2 = time2; T2 = t2(N2)/N2; figure(2); subplot(2,1,1); plot(t2,pulse2) title('PULSE OXIMETRY 2'); xlabel('TIME,seconds'); ylabel('%sat'); pulse2dt = detrend(pulse2); %removes average value PULSE2 = T2*fft(pulse2dt); magpulse2 = abs(PULSE2(1:N2/2)); fd2 = 1/(N2*T2); f2 = (0:N2/2-1)*fd2; subplot(2,1,2); plot(f2,magpulse2); grid AXIS([0 2 min(magpulse2) max(magpulse2)]) title(‘MAGNITUDE SPECTRUM'); xlabel('FREQUENCY, HZ'); ylabel('MAGNITUDE')

9. Tracing and Frequency Spectrum 010203040506070 0 2 4 6 8 10 12 PULSE OXIMETRY 2 TIME,seconds %sat 00.20.40.60.811.21.41.61.82 10 20 30 40 FREQUENCY, HZ MAGNITUDE

10. More MATLAB code load data2.txt; l2 = length(data2); [time2 pulse2] = textread('data2.txt', '%f %f',l2); N2 = length(pulse2); t2 = time2; T2 = t2(N2)/N2; pulse2dt = detrend(pulse2); %removes average value PULSE2 = T2*fft(pulse2dt); magpulse2 = abs(PULSE2(1:N2/2)); fd2 = 1/(N2*T2); f2 = (0:N2/2-1)*fd2; cumsum(magpulse2); k2 = max(cumsum(magpulse2)) k2 = 568.4633 The cumulative sum of the magnitude spectrum over all frequencies is calculated. It is the “new pulsus paradoxus.”

11. Correlation of Data Original PP vs. New PP

12. Conclusion: Clinical Application Based on the correlation of our data with the established pulsus paradoxus values, we conclude that our method is an accurate way to quantitate the pulsus paradoxus, and thus the severity of the disease. Note: While developing our method, we were blinded to the diagnoses. The established pulsus paradoxus values were not made known to us until after we had results.

13. Current Status Work has been completed. More tracings need to be digitized. Further statistical tests will need to be done to verify the method used. Current Work We will be completing final steps of project (final presentation, web page, paper, etc…)

14. Work Completed Mathematical analysis has been completed. Future Work Digitizing more tracings Preparation for final presentation and write- up

15. Future Direction Further studies to validate the algorithm derived from method Design a method to be able to quantify pulsus paradoxus value in real time

16. Acknowledgements VUMC Intensive Care Unit Staff Patrick Norris Dr. Shiavi Dr. Paul Harris Figures used throughout presentation were obtained from presentations given by our advisor References