Kara Bliley

Gina Lee

Allison Powers

Advisor: Tina V. Hartert, M.D., M.P.H.

Quantification of Respiratory Waveform Variations in Pulse

Oximetry Tracings

Background

AC

DC

•Readings are based on pulsatile absorption

•Arterial blood assumed to be the only pulsatile absorbance between light source and photodetector

Pulse oximetry:

Background

• Pulse oximetry is the measure of “functional O2 saturation” which is defined as the percentage of oxyhemoglobin (O2Hb) relative to the total amount of Hb available for binding:

CO-oximeter: fractional SaO2

O2Hb

O2Hb + Hb + COHb + MetHbx 100%

O2Hb

O2Hb + Hbx 100%

Pulse oximeter: functional SaO2

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

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

timeF

unct

iona

l O2

satu

rati

on

Objective

• To develop an algorithm to quantify pulsus paradoxus

Normal arterial pressure trace: Pulsus paradoxus:

Fun

ctio

nal O

2 sa

tura

tion

time

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

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')

Tracing and Frequency Spectrum

0 10 20 30 40 50 60 700

2

4

6

8

10

12PULSE OXIMETRY 2

TIME,seconds

%sa

t

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2

10

20

30

40

FREQUENCY, HZ

MA

GN

ITU

DE

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 valuePULSE2 = 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.”

Correlation of DataNew PP vs. Original PP

y = 0.0685x - 15.048

R2 = 0.6189

0

5

10

15

20

25

30

35

0 200 400 600 800

New PP

Ori

gin

al

PP

Original PP vs. New PP

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.

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…)

Work Completed

• Mathematical analysis has been completed.

Future Work

• Digitizing more tracings

• Preparation for final presentation and write-up

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

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