1

Noninvasive Detection of Cardiac Stressors using the

Photoplethysmograph

Stephen Paul Linder

2

Motivation

Develop noninvasive ways of ascertaining physical health in ambulatory subjects?

Possible sensors Thermometers EKG – used by runners Laser Doppler flowmetry New blood pressure sensors that do not require a arm

cuff Pulse oximeters

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Pulse Oximeters

The pulse oximeter uses changes in reflected or transmitted light to infer volumetric changes

The resulting photoplethysmogram (PPG) gives the temporal variation in blood volume of peripheral tissue

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Photoplethysmogram

0 20 40 60 80 100 120

finger

0 20 40 60 80 100 120

forehead

0 20 40 60 80 100 120

ear

Time (sec)

Standing

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Detecting Hypovolemia

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Background

Currently there is no easy noninvasive way to detect hypovolemia in a subject who is not artificially ventilated or doing paced breathing.

Hypovolemia affects the Respiratory-Induced Variation (RIV) in blood flow in subjects who are mechanically ventilated. Can a reliable automatic detector for hypovolemia be

built for non-ventilated subjects?

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Lower-body negative pressure (LBNP)

Induces hypovolemia by sequestering blood in the hips and lower extremities

Sequesters between 2 and 3 liters of blood at -90 mm Hg

Work done with Victor Convertino and Gary Muniz at the Institute of Surgical Research, Brooks Army Medical Center

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Respiratory-Induced Variation with LBNP of 80 mmHg

688 690 692 694 696 698 700 702 704 -800 -600 -400 -200

0 200 400 600 800

1000

Time (sec)

MaxMin

MinMax

Δtop

MaxMax

Δtop

MinMin

Srise

Sfall

688 690 692 694 696 698 700 702 704 706

70 80

90 100

Time (sec)

He

art

Ra

te (

bp

m)

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Marker M1 for Hypovolemia

688 690 692 694 696 698 700 702 704

Time (sec)

MaxMin

MinMax

Δtop

Δtop > (MaxMin – MinMax)MaxMin – MinMax

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Hypovolemia detections using M1

500 1000 1500 2000 2500-400

-200

0

200

400

600

800

1000

1200

Time (sec)

Δtop MaxMin – MinMax

15 mmHg 30 mmHg 45 mmHg 60 mmHg 70 mmHg 80 mmHg 90 mmHgStop

Trial 2

LBNP

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Marker M2 for Hypovolemia

688 690 692 694 696 698 700 702 704

Time (sec)

Srise

Synchronous rise and fall of top and bottom envelope

Sfall

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M2 details

Rise and fall do not have to be perfectly monotonic Calculate Euclidean distance between data and sorted

data

Use the following parameters Sliding window of length 4 Rise or fall must be more than 40% of median peak

height Euclidean distance to sorted data must be less than

20% of median peak height

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Marker M2 for Hypovolemia

688 690 692 694 696 698 700 702 704

Time (sec)

Srise

Synchronous rise and fall of top and bottom envelope

Sfall

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M1 vs. M2

2010 2020 2030 2040 2050 2060 2070 2080 2090

-400

-200

0

200

400

600

M2 Rising/Falling Envelope

Time (sec)

M1

30 Cardiac Cycles

Trial 3, LBNP = -80 mmHg Metric M1 is detected more often because of longer window

A window of 30 cardiac cycles captures on average four respiratory cycles

M1 more sensitive than M2 M1 is uses robust statistics over a long

window

15

1000 1200 1400 1600 1800 2000 2200 2400 2600

-1000

-800

-600

-400

-200

0

200

400

600

800

1000

Time (sec)

Trial 2

LBNB is reduced to 15, 30, 45, 60, 70, 80 and 90 mmHg every 300 sec starting at 450 sec.

45 mmHg 60 mmHg 70 mmHg 80 mmHg 90 mmHg Stop

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400 500 600 700 800 900 1000-1000

-500

0

500

1000Subject 1 Trial 3 Sensor 1 forehead-holder

400 500 600 700 800 900 1000-1000

-500

0

500

1000Subject 1 Trial 3 Sensor 2 forehead-tape

400 500 600 700 800 900 1000-1000

-500

0

500

1000Subject 1 Trial 3 Sensor 3 fingerPump Down

Forehead: applied with Nonin Holder

Forehead: applied with tape

Finger: applied with clip

Trial 3

Time (sec)

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640 650 660 670 680 690 700-1000

-500

0

500

1000

Subject 1 Trial 3 Sensor 1 forehead-holder

640 650 660 670 680 690 700-1000

-500

0

500

1000

Subject 1 Trial 3 Sensor 2 forehead-tape

640 650 660 670 680 690 700-400

-300

-200

-100

0

100

200

300

400

500Subject 1 Trial 3 Sensor 3 finger

Forehead: applied with Nonin Holder

Forehead: applied with tape

Finger: applied with clip

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Why not frequency analysis?

Time

Fre

quen

cy (

Hz)

Forehead sensor, trial 2

500 1000 1500 2000 2500 30000

0.5

1

1.5

2

2.5

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Frequency contribution of respiration and cardiac cycles

290 300 310 320 3303.15

3.2

3.25

3.3

3.35

3.4

x 104

Time (s)

PPG

inte

nsity

PPG at 0 mmHg

1950 1955 1960 1965 1970 1975 19803.15

3.2

3.25

3.3

3.35

3.4

x 104

Time (s)

PPG

inte

nsity

PPG at -80 mmHg

2440 2445 2450 2455 2460 24653.15

3.2

3.25

3.3

3.35

3.4

x 104

Time (s)

PPG

int

ens

ity

PPG at -90 mmHg

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.60

1

2

3

4

5

6

7

8

9

10

11x 10

5

abs

Pow

er

Frequency (Hz)

Power Spectral Density: 300 seconds, 0 mmHg

Respiratory Peak

HR Peak

0 0.5 1 1.50

1

2

3

4

5

6

7

8

9

10

11x 10

5

abs

Pow

er

Frequency (Hz)

Power Spectral Density at 1950 seconds: -80 mmHg

Respiratory Peak

HR peak

0 0.5 1 1.5 20

1

2

3

4

5

6

7

8

9

10

11x 10

5

abs

Pow

erFrequency (Hz)

Power Spectral Density at 2460 seconds: -90 mmHg

Respiratory Peak

HR Peak

In the frequency domain, the respiratory power (0.15 Hz peak) increases as the heart rate power decreases

80 mmHg 90 mmHg0 mmHg

PPG

Power

Spectral

Density

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Why not frequency analysis?

0 500 1000 1500 2000 2500 3000 3500 0 1 2 3 4 5 6 7 8 9 x 106

Time (s)

PS

D

15 30 LBNP 45 60 70 80 90 mmHg 0

Respiratory power HR power Detector Output LBNP change

The triangles mark where the ratio of the respiratory power to the heart rate power is greater than 0.1.

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Detecting Exercise Induced Stress

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Why?

A cardiologist examining EKG, blood pressure and cardiac output of a healthy subject approaching volitional fatigue would find no markers for cardiac stress

Vigorous exercise is a good model for stress because it produces Hypoperfusion as seen in shock Similar inflammatory and immune response as shock

Hemodynamic stress of exercise can cause task failure

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Bruce Protocol Stress Test

StageTime (min)

km/hr SlopeNumber of Subjects

1 0 2.74 10%

2 3 4.02 12%

3 6 5.47 14%

4 9 6.76 16% 2

5 12 8.05 18% 2

6 15 8.85 20% 5

7 18 9.65 22% 2

A standardized multistage treadmill test for assessing cardiovascular health. Subjects were healthy and athletic and, except for one middle aged

researcher, all in their twenties.

Bruce Protocol Stage Descriptionsand

Distribution of Maximum Stage Reached

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Detecting Spindle Waves (1)

Stage 1Detect all cardiac cycles using a morphologic-based classifier written in Matlab.Stage 2Detect undulations in the envelope of the PPG. This is done by taking the sum of top envelope plus peak height, and then running the same classifier used in Stage 1.

905 910 915 920 925 930Time (sec)

Navajo spindle

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Detecting Spindle Waves (2)

Stage 3Detect motion artifacts

1255 1260 1265 1270 1275

Time (sec)

Pinching ends

AND peak in middle

AND smooth envelop

Envelope too small

Noisy wave that pinches

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Detecting Spindle Waves (4)

Stage 4A classifier was tuned to detect spindle waves using the following metrics to minimize false positives caused by motion artifacts, respiratory-induced variation, etc:

no cardiac cycles with large motion artifacts detected in Stage 3

significant pinching at both beginning and end relatively smooth rise and fall an envelope peak centered in the middle at least five cardiac cycles

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Spindles waves in ear and forehead

970 980 990 1100 1020 1030 1040 1050

Time (sec)

ear

finger

forehead

1000

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Spindle wave occurrences often synchronize with start of new stage

Data from Subject 3, ear PPG

750 800 850 900 950 1000 1050 1100 1150 1200 Time (sec)

Stage 5

Stage 6

Stage 7 Slow

Treadmill

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Spindle waves and Respiration

950 960 970 980 990 1000 1010 Time (sec)

Stage 6

Respiration

PPG

Data from Subject 6, ear PPG and EKG-based impedance pneumography

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0 200 400 600 800 1000 1200 1400

1

2

3

4

5

6

7

8

9

10

11

Time (sec)

Forehead Ear

Treadmill Slow Down

A

B

C

D

F

E

Stage 1 Stage 2 Stage 3 Stage 4 Stage 5 Stage 6 Stage 7

Tri

al N

um

ber

Spindle wave detections in the PPG from forehead and ear pulse oximeters during the stages of the Bruce Protocol stress test.

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0 200 400 600

25 spindles/min

1000 1200

1

2

3

4

5

6

7

8

9

10

11

Time(sec)

Ear

Forehead

Stage1 Stage2 Stage 3 Stage 4 Stage 5 Stage 6 Stage 7

Sub

ject

Num

ber

Frequency of well formed spindle waves for the ear and forehead PPG for all 11 subjects. The number of spindle waves per minute increases with fatigue for at least one of the sensors for all subjects except Subject 3 and 10.

32500 600 700 800 900 1000 1100 1200 1300

1

2

3

4

5

6

7

8

9

10

11

Time (sec)

Minimum peak height for each wave in the upper envelope of the ear PPG. Detected spindle waves are marked. Treadmill

Slowdown

330 200 400 600 800 1000 1200

1

2

3

4

5

6

7

8

9

10

11

Time (sec)

Treadmill Slowdown

Minimum peak height for each wave in the upper envelope of the ear (red and forehead (blue) PPG. Detected spindle waves are marked.

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Improvements

Detect trains of spindle waves instead of single spindle waves

1255 1260 1265 1270 1275

Time (sec)

Pinching ends

AND peak in middle

AND smooth envelop

Envelope too small

Noisy wave that pinches

35

Have you seen this before?

Data from Subject 5, finger PPG

1116 1118 1120 1122 1124 1126 1128 1130 1132 1134 1136

Time (sec)

pinching ends

AND peak in middle

AND smooth envelope

Envelope too small

Noisy waves that pinches

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Acknowledgements

Thanks to Victor Convertino and Gary Muniz at the Institute of

Surgical Research, Brooks Army Medical Center Dr. Kirk Shelly at Yale Medical School Dr. Susan McGrath at ISTS

Collaboration? Contact: spl@alum.mit.edu

DisclaimerThis project was supported under Award No. 2000-DT-CX-K001 from the Office for Domestic Preparedness, U.S. Department of Homeland Security. Points of view in this document are those of the author and do not necessarily represent the official position of the U.S. Department of Homeland Security.

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Pulse Oximetry OverviewPulse Oximetry Overview

Uses the different light absorption properties of HbO2 and Hb to measure heart rate, oxygen saturation (SpO2) and pleth waveform

Two LED’s of different wavelength Red 660 nm Infrared 940 nm

HbO2 absorbs less red and more infrared than HB.

Hb absorbs less infrared and more red than HbO2.

Two equations, two unknowns… we can solve for SpO2

HbHbO

HbOp CC

COS

2

2

2

Extinction Curve

0.00E+00

2.00E-04

4.00E-04

6.00E-04

8.00E-04

1.00E-03

600 700 800 900 1000 1100

Wavelength (nm)

Ab

sorp

tio

n

A(Hb)

A(HbO)

The pleth waveform consist of the IR tracing.

Indirect measurement of blood volume under the sensor