noninvasive detection of cardiac stressors using the photoplethysmograph
DESCRIPTION
Noninvasive Detection of Cardiac Stressors using the Photoplethysmograph. Stephen Paul Linder. Motivation. Develop noninvasive ways of ascertaining physical health in ambulatory subjects? Possible sensors Thermometers EKG – used by runners Laser Doppler flowmetry - PowerPoint PPT PresentationTRANSCRIPT
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Noninvasive Detection of Cardiac Stressors using the
Photoplethysmograph
Stephen Paul Linder
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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
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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: [email protected]
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