Pulse Oximeter to Monitor Breathing Rate

Sara Guillén Fernández-Micheltorena

Javier García Baroja

María Lancho Lavilla

Patrice Gill

Index

• Background
• What we have vs what we need
• 3D design
• Circuit
• Output and signal analysis
• Next steps

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Background

• When the patients are using the apps, their test scores might be affected by stress.
• Hyperventilation or overbreathing (resulting in lower end-tidal CO2) occurs in patients when they are subject to a “stressful” environment.¹
• However, respiration on its own is not a sufficient indicator of stress
• Measuring both heart rate variability (HRV) and respiration rate would allow us to determine whether the patient is undergoing stress or not.
• To measure the respiration rate, we decided to use a plethysmographic (PPG) sensor: a pulse oximeter for the ear lobe, in order to avoid obstructing the patients movement.

What we have now:

What we have to do:

• We have already developed a model (3D model and circuitry) using a red LED.

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• We obtained some output but have not obtained breathing rate yet.

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• After further research, found that a red LED is enough to obtain heart rate, but both red and infrared LEDs are needed to obtain breathing rate.^4,5

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• We have to implement the infrared LED in the circuitry, modify the 3D design to fit both and process the signal.

Current state

3D Model

• We found we might need Red & infrared LEDs
• To try: additional piece to check if both are needed
• Straight Solution: make the hole bigger in the existing device

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• To keep the LEDs inside the added piece, we made a cover with a hole for joining the leads to the rest of the circuit

3D Model

The arrow indicates where the new piece would be attached

Circuit

• Chose a pulse oximeter as our PPG sensor, as there have been previous studies monitoring respiratory rate using a pulse oximeter ²
• The basics of a pulse oximeter involve the use of a light emitting diode (LED) and a phototransistor placed on either side of the object
• LED emits visible light and the phototransistor picks up the light and adjusts the output current accordingly
• We decided to use both an LED as well as Infrared LED to increase the accuracy.
• This enables us to monitor the concentration of oxygen in the blood.

Output and Signal Analysis

• The output from our device is currently sporadic spikes
• We are going to perform a fourier transform on this data in order to obtain a power spectrum that will show us what frequency this person is breathing at
• We are currently using an Arduino Circuit board which we can then code in Arduino C

Next steps

• Optimize signal acquisition and analysis process.
• Combine with heart rate variability results in order to detect when our patient is stressed.
• Finalize design and make it look and feel nicer (PCB).
• Work with iOS and android subteams to properly display data and findings in an application.
• Measure more parameters that are indicative of stress (perspiration, pupil dilation, etc).

References

1. Bryant, Richard A., et al. "A multisite study of initial respiration rate and heart rate as predictors of posttraumatic stress disorder." The Journal of Clinical Psychiatry (2008).
2. Johnston, W. S., & Mendelson, Y. (2004, September). Extracting breathing rate information from a wearable reflectance pulse oximeter sensor. In The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (Vol. 2, pp. 5388-5391). IEEE.
3. Takatani, S., Davies, C., Sakakibara, N., Zurick, A., Kraenzler, E., Golding, L. R., ... & DeBakey, M. E. (1992). Experimental and clinical evaluation of a noninvasive reflectance pulse oximeter sensor. Journal of clinical monitoring, 8(4), 257-266.
4. Leonard P, Beattie TF, Addison PS, et al Standard pulse oximeters can be used to monitor respiratory rate Emergency Medicine Journal 2003;20:524-525.
5. T.L. Rusch, R. Sankar, J.E. Scharf,Signal processing methods for pulse oximetry, Computers in Biology and Medicine, Volume 26, Issue 2, 1996, Pages 143-159, ISSN 0010-4825,