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Biomedical Optics Express

Biomedical Optics Express

  • Editor: Joseph A. Izatt
  • Vol. 3, Iss. 12 — Dec. 1, 2012
  • pp: 3325–3331
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A miniature optical breathing sensor

Jinesh Mathew, Yuliya Semenova, and Gerald Farrell  »View Author Affiliations


Biomedical Optics Express, Vol. 3, Issue 12, pp. 3325-3331 (2012)
http://dx.doi.org/10.1364/BOE.3.003325


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Abstract

We demonstrate a novel miniature optical breathing sensor based on an Agarose infiltrated photonic crystal fiber interferometer. The sensor detects the variation in relative humidity that occurs between inhaled and exhaled breath. The sensor interrogation system can determine the breathing pattern in real time and can also predict the breathing rate and the breathing status during respiration. The sensor is suitable for monitoring patients during a magnetic resonance imaging scan where use of sedatives and anesthetics necessitates breathing monitoring; electronic sensors are not suitable in such an environment and a visual observation of the patient's respiratory efforts is often difficult.

© 2012 OSA

1. Introduction

Breathing is a human vital sign; it is the process that moves varying volumes of air into and out of the lungs. This is required to provide an adequate oxygen (O2) supply to meet the energy production requirements of the body and maintain a suitable acid-base status by removing carbon dioxide (CO2) from the body. The act of normal breathing has a relatively constant rate. The rate is noted by observing the frequency of breathing. The number of breaths per minute is called the breath rate.

Another approach to breath rate monitoring is using fiber-based humidity or temperature sensors placed close to the patient’s nose or mouth since the air exhaled has higher humidity and is warmer than the inhaled air [4

4. S. Akita, A. Seki, and K. Watanabe, “A monitoring of breathing using a hetero-core optical fiber sensor,” Proc. SPIE 7981, 79812W, 79812W-6 (2011). [CrossRef]

,17

17. S. Muto, H. Sato, and T. Hosaka, “Optical humidity sensor using fluorescent plastic fiber and its application to breathing-condition monitor,” Jpn. J. Appl. Phys. 33(Part 1, No. 10), 6060–6064 (1994). [CrossRef]

21

21. F. C. Favero, J. Villatoro, and V. Pruneri, “Microstructured optical fiber interferometric breathing sensor,” J. Biomed. Opt. 17(3), 037006 (2012). [CrossRef] [PubMed]

]. Air in the lungs is essentially saturated with water at body temperature of 37°C [22

22. Natural Science Forum, “Relative humidity of human exhaled breath,” July 3, 2004, http://www.natscience.com/Uwe/Forum.aspx/bio/233/Relative-humidity-of-human-exhaled-breath.

]. Most of that water is supplied by evaporation from the membranes lining the nose and as a result, these surfaces are cooled a bit. When one exhales, the breath passes over these cooled surfaces and loses some of its moisture, thus conserving at least some of the body water. However, the air exhaled is still saturated (100% relative humidity) or very close to it.

Recently we have developed a miniature optical humidity sensor based on an Agarose infiltrated photonic crystal fiber interferometer (AI-PCFI) [23

23. J. Mathew, Y. Semenova, and G. Farrell, “Relative humidity sensor based on an Agarose infiltrated photonic crystal fiber interferometer,” IEEE J. Sel. Top. Quantum Electron. 18(5), 1553–1559 (2012). [CrossRef]

]. Compared to the existing optical-fiber-based humidity sensors, the sensor proposed in [23

23. J. Mathew, Y. Semenova, and G. Farrell, “Relative humidity sensor based on an Agarose infiltrated photonic crystal fiber interferometer,” IEEE J. Sel. Top. Quantum Electron. 18(5), 1553–1559 (2012). [CrossRef]

] has the advantages of a very compact size, high resolution, fast response time, ease of fabrication. The sensor head is also low cost and thus offers the potential to be disposable. Disposable sensors are required while monitoring breathing from the nose or mouth because exhaled breath condensate can contaminate the sensor head making the sensor unsuitable to reuse on another patient. The end-type sensor head configuration offers the advantages of being able to operate in environments which demand a compact probe-type sensor and also reduced system complexity as only one interconnecting fiber is needed. These advantages underpin the motivation to investigate disposable sensors for breathing monitoring in a clinical situation using AI-PCFI. In this paper we demonstrate a breathing pattern and breathing rate sensor developed using an AI-PCFI. The sensor registers a change in the received optical power as a function of time as the air is exhaled making it suitable for monitoring breathing patterns. It has been demonstrated that by appropriate signal processing one can determine the breathing rate and the breathing status during expiration. The demonstrated sensor is also suitable for monitoring a decrease in respiratory rate, hypoxemia, or airway obstruction associated with the use of sedatives and anesthetics in the MRI environment.

2. Experimental demonstration and discussion

The complete experimental sensor system is composed of a light source - tunable laser (Anritsu, Tunics plus CL/WB), a fiber optic coupler/circulator (FOC), the AI-PCFI (relative humidity sensor), an optical detector (PX Instrument Technology, PX2000-306) and a PC with a breath analysis application program, as shown in Fig. 1
Fig. 1 Schematic diagram of a fiber optic breath sensor system, (upper) microscope image of an AI-PCFI and (lower) a photograph of the mask placed on the volunteer’s face showing the position of the sensor inside the mask (dotted line). (FOC- Fiber optic circulator, SMF-Single mode fiber, AI-PCFI-Agarose infiltrated-photonic crystal fiber interferometer, PC/BAAP-Personal computer/Breath analysis application program; dotted arrows represent the light path).
. The AI-PCFI is composed of a small length of Photonic crystal fiber (PCF) fusion spliced to the end of a standard single mode fiber (SMF). The PCF in the sensor head has a microhole collapsed region near the splicing point and the free end of the PCF is infiltrated with Agarose.

In our experiment the optical power change obtained due to the spectral shift induced by changes in humidity during the breathing cycle is monitored. For this purpose a tunable laser output at a wavelength of 1550 nm is fed to the AI-PCFI via the FOC and the reflected optical power from the AI-PCFI is measured using a detector. Accurate measurement of pulmonary ventilation or breathing often requires the use of devices such as masks or mouthpieces coupled to the airway opening. For this purpose the device was mounted in an inexpensive, disposable plastic oxygen mask which in turn was attached to a volunteer’s nose and mouth and secured with the elasticized headband of the mask (See Fig. 1.). The sensor was set in such a way that it was kept approximately 5 cm from the patient’s nose to avoid condensation on the device. We estimated the distance between the tip of the nose and the tip of the sensor using a conventional measuring scale. In our study this distance varies depending on the patient’s nose size because the sensor is fixed on the oxygen mask (Fig. 1). However in practice this distance can be maintained by fixing the sensor on to a moving platform inside the oxygen mask or by suitably fixing the sensor inside the mask with a prior knowledge of the patient nose size. The normal baseline RH signal depends on the room humidity and also on the distance of the sensor from the patient’s nose. The time-dependent RH signal when the patient is breathing normally is equal to the ambient RH (< 80% RH) during inhalation and ~100% RH during exhalation. We have not investigated the detail packaging of the AI-PCFI in this study. But it is important to point out that before using the sensor in a clinical environment the sensor would have to be packaged inside a suitable plastic tube to prevent the transmitted light reaching the volunteer’s eyes, face or skin.

The optical power received by the detector is acquired in real time (with a maximum delay of the order of ms) using the breath analysis application program based on LabVIEW platform. Figure 2
Fig. 2 Screen shot of the user interface of the breath analysis application program showing the continuous breathing response. Upper plot shows the breathing pattern (inhalation → peaks and exhalation → valleys) and the lower plot indicates the derived breathing state (inhalation → low and exhalation → high). Units of the axis are: upper plot x-axis is seconds multiplied by 10 and y-axis is dB; lower plot x-axis is seconds multiplied by 10 and y-axis is arbitrary units.
. shows a screen shot of the user interface of the breath analysis application program, for the case of a regular breathing pattern. The real time breathing response of the sensor system is displayed by the application program user interface. During breath expiration due to an increase of the ambient humidity in the vicinity of the sensor, the reflected power received by the detector decreases [23

23. J. Mathew, Y. Semenova, and G. Farrell, “Relative humidity sensor based on an Agarose infiltrated photonic crystal fiber interferometer,” IEEE J. Sel. Top. Quantum Electron. 18(5), 1553–1559 (2012). [CrossRef]

]. In the real time breathing response trace there are valleys which represent exhalation and peaks which represent inhalation. The lower plot in Fig. 2. is the breathing state indicator calculated from the breathing pattern using a preset threshold. It has two states, a high level represents the state after air is exhaled and a low indicates the state after air is inhaled. Each time when the power decreases below the preset threshold value, the breath count (BC) is incremented once and it is displayed in the user interface of the program. Since breath rate is the number of breaths per minute, after every 60 seconds the counter is reset. Average Breath Rate (BR) is calculated as BR = (BC*60)/ET where ET is the elapsed time since the last reset. For user convenience the elapsed time, breath rate and breath status are also displayed in the user interface/front panel of the application. Breathing status is described as follows ‘NORMAL’ when BR is between 10 and 20; ‘LOW’ when BR < 10; ‘HIGH’ when BR > 20.

Figure 3
Fig. 3 Screen shot of the user interface of the breath analysis application program showing a breath-hold. Upper plot shows the breathing pattern (inhalation → peaks and exhalation → valleys) and the lower plot indicates the derived breathing state (inhalation → low and exhalation → high). Units of the axis are: upper plot x-axis is seconds multiplied by 10 and y-axis is dB; lower plot x-axis is seconds multiplied by 10 and y-axis is arbitrary units.
shows a screen shot of the user interface for an irregular breathing pattern, where the subject ceases breathing for a few seconds, in order to demonstrate that our sensor system is capable of monitoring breathing abnormalities in real time. The valleys shown in the upper plots of Figs. 2 and 3 are due to the air breathed out by the volunteer and the area inside each of these valleys can be correlated with the amount of air breathed out. Thus by applying an appropriate signal analysis and calibration, the breathing air volume could be estimated from our sensor response. The value of breathing volume could be used as an indicator of potential respiratory dysfunction and subject’s pulmonary health status.

Since the dimensions of the sensor head are very small, the breathing response is unaffected by the turbulence and vibrations that result from air flow during breathing and other mechanical effects that may occur due to the movement of a patient. Given that the interrogation system measures the reflected intensity of light a possible source of error could arise from random variations in the received power resulting from variations in the bend radius at any bends in the fiber connecting the sensor to the interrogation system. To prevent such a failure the fiber cable connected to the sensor would need to be packaged to avoid small bend radii and significant changes in bending radius. Other fluctuation sources are the variations in the ambient RH, the distance between patient’s nose and the sensor and the fluctuations in the RH of the exhaled air. The source power fluctuation, wavelength drift and the detector noise might also contribute to the measurement errors in the system demonstrated. However selecting a threshold power equivalent to a higher RH than the ambient RH should overcome the effect of these fluctuation sources on monitoring the breath rate. Finally it should be point out that failure of the sensor may occur if a person coughs or contaminates the device with fluids (saliva, sputum, etc.). However, these issues can be avoided or overcome by embedding the device in a nasal clip, or by adequate packaging.

Because respiratory depression and upper airway obstruction are frequent complications associated with the use of sedatives and anesthetics, monitoring the respiratory rate, hypoxemia, and the detection of airway obstruction are important during the administration of these drugs. This is particularly important in the MRI environment because visual observation of the patient's respiratory efforts is often difficult. Also since the anesthetist cannot accompany the patient, it is essential that the patient is monitored remotely from the neighboring control room. The magnetic fields can interfere with electrical equipment, meaning that conventional electronic sensors cannot be used during the MRI scan. The sensor demonstrated in this paper is a potential solution for these problems. Finally while a tunable laser source was used in the demonstration described here, in order to reduce the cost of the system for real-world applications the tunable laser can be readily replaced with a low cost fixed wavelength laser diode. A high power change of >5 dB will be obtained using our sensor during breathing even in event of the source intensity and/or center wavelength drift. In addition a shift in the dynamic signal power level of the sensor can be addressed easily in the LabVIEW program by suitably shifting the threshold, so that the source power fluctuation and the wavelength drift of a low cost source will not degrade the accuracy of the breath rate monitoring.

References and links

1.

S. R. Braun, Clinical Methods, The History, Physical, and Laboratory Examinations (Butterworth Publishers, Stoneham, MA, 1990), Chap. 43.

2.

M. Folke, L. Cernerud, M. Ekström, and B. Hök, “Critical review of non-invasive respiratory monitoring in medical care,” Med. Biol. Eng. Comput. 41(4), 377–383 (2003). [CrossRef] [PubMed]

3.

L. Schulte-Uentrop and M. S. Goepfert, “Anaesthesia or sedation for MRI in children,” Curr. Opin. Anaesthesiol. 23(4), 513–517 (2010). [CrossRef] [PubMed]

4.

S. Akita, A. Seki, and K. Watanabe, “A monitoring of breathing using a hetero-core optical fiber sensor,” Proc. SPIE 7981, 79812W, 79812W-6 (2011). [CrossRef]

5.

P. Várady, T. Micsik, S. Benedek, and Z. Benyó, “A novel method for the detection of apnea and hypopnea events in respiration signals,” IEEE Trans. Biomed. Eng. 49(9), 936–942 (2002). [CrossRef] [PubMed]

6.

F. Q. Al-Khalidi, R. Saatchi, D. Burke, H. Elphick, and S. Tan, “Respiration rate monitoring methods: a review,” Pediatr. Pulmonol. 46(6), 523–529 (2011). [CrossRef] [PubMed]

7.

R. Shellock and D. Services, Inc., and F. G. Shellock, “Monitoring patients in the MRI environment,” (2012), http://www.mrisafety.com/safety_article.asp?subject=40.

8.

C. T. Results, “Optical sensors make MRI scans safer,” Science Daily, 20 September 2008, www.sciencedaily.com/releases/2008/09/080918091609.htm.

9.

M. F. Dempsey and B. Condon, “Thermal injuries associated with MRI,” Clin. Radiol. 56(6), 457–465 (2001). [CrossRef] [PubMed]

10.

A. Babchenko, B. Khanokh, Y. Shomer, and M. Nitzan, “Fiber optic sensor for the measurement of respiratory chest circumference changes,” J. Biomed. Opt. 4(2), 224–229 (1999). [CrossRef] [PubMed]

11.

G. Wehrle, P. Nohama, H. J. Kalinowski, P. I. Torres, and L. C. G. Valente, “A fiber optic Bragg grating strain sensor for monitoring ventilatory movements,” Meas. Sci. Technol. 12(7), 805–809 (2001). [CrossRef]

12.

A. Grillet, D. Kinet, J. Witt, M. Schukar, K. Krebber, F. Pirotte, and A. Depré, “Optical fiber sensors embedded into medical textiles for healthcare monitoring,” IEEE Sens. J. 8(7), 1215–1222 (2008). [CrossRef]

13.

M. Nishyama, M. Miyamoto, and K. Watanabe, “Respiration and body movement analysis during sleep in bed using hetero-core fiber optic pressure sensors without constraint to human activity,” J. Biomed. Opt. 16(1), 017002 (2011). [CrossRef] [PubMed]

14.

A. F. Silva, J. P. Carmo, P. M. Mendes, and J. H. Correia, “Simultaneous cardiac and respiratory frequency measurement based on a single fiber Bragg grating sensor,” Meas. Sci. Technol. 22(7), 075801 (2011). [CrossRef]

15.

J. Witt, F. Narbonneau, M. Schukar, K. Krebber, J. De Jonckheere, M. Jeanne, D. Kinet, B. Paquet, A. Depre, L. T. D’Angelo, T. Thiel, and R. Logier, “Medical textiles with embedded fiber optic sensors for monitoring of respiratory movement,” IEEE Sens. J. 12(1), 246–254 (2012). [CrossRef]

16.

L. Mohanty and K. S. C. Kuang, “A breathing rate sensor with plastic optical fiber,” Appl. Phys. Lett. 97(7), 073703 (2010). [CrossRef]

17.

S. Muto, H. Sato, and T. Hosaka, “Optical humidity sensor using fluorescent plastic fiber and its application to breathing-condition monitor,” Jpn. J. Appl. Phys. 33(Part 1, No. 10), 6060–6064 (1994). [CrossRef]

18.

F. J. Arregui, Y. Liu, I. R. Matias, and R. O. Claus, “Optical fiber humidity sensor using a nano Fabry–Perot cavity formed by the ionic self-assembly method,” Sens. Actuators B Chem. 59(1), 54–59 (1999). [CrossRef]

19.

Y. Kang, H. Ruan, Y. Wang, F. J. Arregui, I. R. Matias, and R. O. Claus, “Nanostructured optical fibre sensors for breathing airflow monitoring,” Meas. Sci. Technol. 17(5), 1207–1210 (2006). [CrossRef]

20.

W. J. Yoo, K. W. Jang, J. K. Seo, J. Y. Heo, J. S. Moon, J. H. Jun, J. Y. Park, and B. Lee, “Development of optical fiber-based respiration sensor for noninvasive respiratory monitoring,” Opt. Rev. 18(1), 132–138 (2011). [CrossRef]

21.

F. C. Favero, J. Villatoro, and V. Pruneri, “Microstructured optical fiber interferometric breathing sensor,” J. Biomed. Opt. 17(3), 037006 (2012). [CrossRef] [PubMed]

22.

Natural Science Forum, “Relative humidity of human exhaled breath,” July 3, 2004, http://www.natscience.com/Uwe/Forum.aspx/bio/233/Relative-humidity-of-human-exhaled-breath.

23.

J. Mathew, Y. Semenova, and G. Farrell, “Relative humidity sensor based on an Agarose infiltrated photonic crystal fiber interferometer,” IEEE J. Sel. Top. Quantum Electron. 18(5), 1553–1559 (2012). [CrossRef]

24.

J. Mathew, Y. Semenova, G. Rajan, and G. Farrell, “Humidity sensor based on photonic crystal fibre interferometer,” Electron. Lett. 46(19), 1341–1343 (2010). [CrossRef]

25.

J. Mathew, Y. Semenova, and G. Farrell, “Photonic crystal fiber interferometer for dew detection,” J. Lightwave Technol. 30(8), 1150–1155 (2012). [CrossRef]

26.

J. Mathew, Y. Semenova, and G. Farrell, “Photonic crystal fiber interferometer for humidity sensing,” in Photonic Crystals—Introduction, Applications and Theory, A. Massaro, ed. (InTech, 2012), Chap. 8.

27.

J. Mathew, Y. Semenova, and G. Farrell, “A fiber bend based humidity sensor with a wide linear range and fast measurement speed,” Sens. Actuators A Phys. 174, 47–51 (2012). [CrossRef]

OCIS Codes
(060.2370) Fiber optics and optical communications : Fiber optics sensors
(120.3180) Instrumentation, measurement, and metrology : Interferometry
(170.1610) Medical optics and biotechnology : Clinical applications
(170.4580) Medical optics and biotechnology : Optical diagnostics for medicine
(060.5295) Fiber optics and optical communications : Photonic crystal fibers

ToC Category:
Biosensors and Molecular Diagnostics

History
Original Manuscript: July 3, 2012
Revised Manuscript: October 24, 2012
Manuscript Accepted: October 25, 2012
Published: November 26, 2012

Citation
Jinesh Mathew, Yuliya Semenova, and Gerald Farrell, "A miniature optical breathing sensor," Biomed. Opt. Express 3, 3325-3331 (2012)
http://www.opticsinfobase.org/boe/abstract.cfm?URI=boe-3-12-3325


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References

  1. S. R. Braun, Clinical Methods, The History, Physical, and Laboratory Examinations (Butterworth Publishers, Stoneham, MA, 1990), Chap. 43.
  2. M. Folke, L. Cernerud, M. Ekström, and B. Hök, “Critical review of non-invasive respiratory monitoring in medical care,” Med. Biol. Eng. Comput.41(4), 377–383 (2003). [CrossRef] [PubMed]
  3. L. Schulte-Uentrop and M. S. Goepfert, “Anaesthesia or sedation for MRI in children,” Curr. Opin. Anaesthesiol.23(4), 513–517 (2010). [CrossRef] [PubMed]
  4. S. Akita, A. Seki, and K. Watanabe, “A monitoring of breathing using a hetero-core optical fiber sensor,” Proc. SPIE7981, 79812W, 79812W-6 (2011). [CrossRef]
  5. P. Várady, T. Micsik, S. Benedek, and Z. Benyó, “A novel method for the detection of apnea and hypopnea events in respiration signals,” IEEE Trans. Biomed. Eng.49(9), 936–942 (2002). [CrossRef] [PubMed]
  6. F. Q. Al-Khalidi, R. Saatchi, D. Burke, H. Elphick, and S. Tan, “Respiration rate monitoring methods: a review,” Pediatr. Pulmonol.46(6), 523–529 (2011). [CrossRef] [PubMed]
  7. R. Shellock and D. Services, Inc., and F. G. Shellock, “Monitoring patients in the MRI environment,” (2012), http://www.mrisafety.com/safety_article.asp?subject=40 .
  8. C. T. Results, “Optical sensors make MRI scans safer,” Science Daily, 20 September 2008, www.sciencedaily.com/releases/2008/09/080918091609.htm .
  9. M. F. Dempsey and B. Condon, “Thermal injuries associated with MRI,” Clin. Radiol.56(6), 457–465 (2001). [CrossRef] [PubMed]
  10. A. Babchenko, B. Khanokh, Y. Shomer, and M. Nitzan, “Fiber optic sensor for the measurement of respiratory chest circumference changes,” J. Biomed. Opt.4(2), 224–229 (1999). [CrossRef] [PubMed]
  11. G. Wehrle, P. Nohama, H. J. Kalinowski, P. I. Torres, and L. C. G. Valente, “A fiber optic Bragg grating strain sensor for monitoring ventilatory movements,” Meas. Sci. Technol.12(7), 805–809 (2001). [CrossRef]
  12. A. Grillet, D. Kinet, J. Witt, M. Schukar, K. Krebber, F. Pirotte, and A. Depré, “Optical fiber sensors embedded into medical textiles for healthcare monitoring,” IEEE Sens. J.8(7), 1215–1222 (2008). [CrossRef]
  13. M. Nishyama, M. Miyamoto, and K. Watanabe, “Respiration and body movement analysis during sleep in bed using hetero-core fiber optic pressure sensors without constraint to human activity,” J. Biomed. Opt.16(1), 017002 (2011). [CrossRef] [PubMed]
  14. A. F. Silva, J. P. Carmo, P. M. Mendes, and J. H. Correia, “Simultaneous cardiac and respiratory frequency measurement based on a single fiber Bragg grating sensor,” Meas. Sci. Technol.22(7), 075801 (2011). [CrossRef]
  15. J. Witt, F. Narbonneau, M. Schukar, K. Krebber, J. De Jonckheere, M. Jeanne, D. Kinet, B. Paquet, A. Depre, L. T. D’Angelo, T. Thiel, and R. Logier, “Medical textiles with embedded fiber optic sensors for monitoring of respiratory movement,” IEEE Sens. J.12(1), 246–254 (2012). [CrossRef]
  16. L. Mohanty and K. S. C. Kuang, “A breathing rate sensor with plastic optical fiber,” Appl. Phys. Lett.97(7), 073703 (2010). [CrossRef]
  17. S. Muto, H. Sato, and T. Hosaka, “Optical humidity sensor using fluorescent plastic fiber and its application to breathing-condition monitor,” Jpn. J. Appl. Phys.33(Part 1, No. 10), 6060–6064 (1994). [CrossRef]
  18. F. J. Arregui, Y. Liu, I. R. Matias, and R. O. Claus, “Optical fiber humidity sensor using a nano Fabry–Perot cavity formed by the ionic self-assembly method,” Sens. Actuators B Chem.59(1), 54–59 (1999). [CrossRef]
  19. Y. Kang, H. Ruan, Y. Wang, F. J. Arregui, I. R. Matias, and R. O. Claus, “Nanostructured optical fibre sensors for breathing airflow monitoring,” Meas. Sci. Technol.17(5), 1207–1210 (2006). [CrossRef]
  20. W. J. Yoo, K. W. Jang, J. K. Seo, J. Y. Heo, J. S. Moon, J. H. Jun, J. Y. Park, and B. Lee, “Development of optical fiber-based respiration sensor for noninvasive respiratory monitoring,” Opt. Rev.18(1), 132–138 (2011). [CrossRef]
  21. F. C. Favero, J. Villatoro, and V. Pruneri, “Microstructured optical fiber interferometric breathing sensor,” J. Biomed. Opt.17(3), 037006 (2012). [CrossRef] [PubMed]
  22. Natural Science Forum, “Relative humidity of human exhaled breath,” July 3, 2004, http://www.natscience.com/Uwe/Forum.aspx/bio/233/Relative-humidity-of-human-exhaled-breath .
  23. J. Mathew, Y. Semenova, and G. Farrell, “Relative humidity sensor based on an Agarose infiltrated photonic crystal fiber interferometer,” IEEE J. Sel. Top. Quantum Electron.18(5), 1553–1559 (2012). [CrossRef]
  24. J. Mathew, Y. Semenova, G. Rajan, and G. Farrell, “Humidity sensor based on photonic crystal fibre interferometer,” Electron. Lett.46(19), 1341–1343 (2010). [CrossRef]
  25. J. Mathew, Y. Semenova, and G. Farrell, “Photonic crystal fiber interferometer for dew detection,” J. Lightwave Technol.30(8), 1150–1155 (2012). [CrossRef]
  26. J. Mathew, Y. Semenova, and G. Farrell, “Photonic crystal fiber interferometer for humidity sensing,” in Photonic Crystals—Introduction, Applications and Theory, A. Massaro, ed. (InTech, 2012), Chap. 8.
  27. J. Mathew, Y. Semenova, and G. Farrell, “A fiber bend based humidity sensor with a wide linear range and fast measurement speed,” Sens. Actuators A Phys.174, 47–51 (2012). [CrossRef]

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