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Noninvasive Detection of Cardiac Stressors using the Photoplethysmograph

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

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Noninvasive Detection of Cardiac Stressors using the Photoplethysmograph

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  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

  3. 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

  4. Photoplethysmogram Standing finger 0 20 40 60 80 100 120 forehead 0 20 40 60 80 100 120 ear 0 20 40 60 80 100 120 Time (sec)

  5. Detecting Hypovolemia

  6. 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?

  7. 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

  8. Respiratory-Induced Variationwith LBNP of 80 mmHg

  9. Marker M1 for Hypovolemia Δtop MaxMin MinMax 688 690 692 694 696 698 700 702 704 Time (sec) MaxMin – MinMax Δtop > (MaxMin – MinMax)

  10. 15 mmHg 80 mmHg 30 mmHg 45 mmHg 70 mmHg 60 mmHg 90 mmHg Stop Hypovolemia detections using M1 Δtop MaxMin – MinMax 1200 1000 800 600 Trial 2 400 200 0 LBNP -200 -400 500 1000 1500 2000 2500 Time (sec)

  11. Srise Marker M2 for Hypovolemia Sfall 688 690 692 694 696 698 700 702 704 Time (sec) Synchronous rise and fall of top and bottom envelope

  12. 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

  13. Srise Marker M2 for Hypovolemia Sfall 688 690 692 694 696 698 700 702 704 Time (sec) Synchronous rise and fall of top and bottom envelope

  14. M2 Rising/Falling Envelope M1 vs. M2 M1 600 400 200 0 -200 -400 2010 2020 2030 2040 2050 2060 2070 2080 2090 Time (sec) 30 Cardiac Cycles • M1 more sensitive than M2 • M1 is uses robust statistics over a long window • Trial 3, LBNP = -80 mmHg • Metric M1is detected more often because of longer window • A window of 30 cardiac cycles captures on average four respiratory cycles

  15. 45 mmHg 60 mmHg 70 mmHg 80 mmHg 90 mmHg Stop Trial 2

  16. Trial 3 Pump Down Time (sec)

  17. Why not frequency analysis?

  18. Frequency contribution of respiration and cardiac cycles 0 mmHg 80 mmHg 90 mmHg PPG Power Spectral Density In the frequency domain, the respiratory power (0.15 Hz peak) increases as the heart rate power decreases

  19. Why not frequency analysis?

  20. Detecting Exercise Induced Stress

  21. 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

  22. Bruce Protocol Stress Test • 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 DescriptionsandDistribution of Maximum Stage Reached

  23. Navajo spindle 905 910 915 920 925 930 Time (sec) Detecting Spindle Waves (1) Stage 1 Detect all cardiac cycles using a morphologic-based classifier written in Matlab. Stage 2 Detect 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.

  24. Pinching ends AND peak in middle AND smooth envelop Envelope too small Noisy wave that pinches 1255 1260 1265 1270 1275 Time (sec) Detecting Spindle Waves (2) Stage 3 Detect motion artifacts

  25. Detecting Spindle Waves (4) Stage 4 A 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

  26. Spindles waves in ear and forehead

  27. Spindle wave occurrences often synchronize with start of new stage Slow Treadmill Stage 5 Stage 6 Stage 7 750 800 850 900 950 1000 1050 1100 1150 1200 Time (sec) Data from Subject 3, ear PPG

  28. Spindle waves and Respiration Stage 6 PPG Respiration 950 960 970 980 990 1000 1010 Time (sec) Data from Subject 6, ear PPG and EKG-based impedance pneumography

  29. Stage 1 Stage 2 Stage 3 Stage 4 Stage 5 Stage 6 Stage 7 A 11 10 B C 9 8 7 Trial Number 6 D 5 E 4 3 F Forehead 2 Ear Treadmill Slow Down 1 200 400 600 800 1000 1200 1400 0 Time (sec)

  30. 11 10 9 8 7 6 5 4 3 2 1 500 600 700 800 900 1000 1100 1200 1300 Time (sec)

  31. 11 10 9 8 7 6 5 4 3 2 1 0 200 400 600 800 1000 1200 Time (sec)

  32. Pinching ends AND peak in middle AND smooth envelop Envelope too small Noisy wave that pinches 1255 1260 1265 1270 1275 Time (sec) Improvements • Detect trains of spindle waves instead of single spindle waves

  33. Have you seen this before? pinching ends AND peak in middle AND smooth envelope Envelope too small Noisy waves that pinches 1116 1118 1120 1122 1124 1126 1128 1130 1132 1134 1136 Time (sec) Data from Subject 5, finger PPG

  34. 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 Disclaimer This 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.

  35. Pulse 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 • The pleth waveform consist of the IR tracing. • Indirect measurement of blood volume under the sensor

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