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MouthLab: A Tricorder Concept Optimized for Rapid Medical Assessment

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Abstract

The goal of rapid medical assessment (RMA) is to estimate the general health of a patient during an emergency room or a doctor’s office visit, or even while the patient is at home. Currently the devices used during RMA are typically “all-in-one” vital signs monitors. They require time, effort and expertise to attach various sensors to the body. A device optimized for RMA should instead require little effort or expertise to operate and be able to rapidly obtain and consolidate as much information as possible. MouthLab is a battery powered hand-held device intended to acquire and evaluate many measurements such as non-invasive blood sugar, saliva and respiratory biochemistry. Our initial prototype acquires standard vital signs: pulse rate (PR), breathing rate (BR), temperature (T), blood oxygen saturation (SpO2), blood pressure (BP), and a three-lead electrocardiogram. In our clinical study we tested the device performance against the measurements obtained with a standard patient monitor. 52 people participated in the study. The measurement errors were as follows: PR: −1.7 ± 3.5 BPM, BR: 0.4 ± 2.4 BPM, T: −0.4 ± 1.24 °F, SpO2: −0.6 ± 1.7%. BP systolic: −1.8 ± 12 mmHg, BP diastolic: 0.6 ± 8 mmHg. We have shown that RMA can be easily performed non-invasively by patients with no prior training.

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References

  1. Banaee, H., M. U. Ahmed, and A. Loutfi. Data mining for wearable sensors in health monitoring systems: a review of recent trends and challenges. Sensors (Basel) 13(12):17472–17500, 2013. doi:10.3390/s131217472.

    Article  CAS  Google Scholar 

  2. Caduff, A., M. S. Talary, M. Mueller, F. Dewarrat, J. Klisic, M. Donath, L. Heinemann, and W. A. Stahel. Non-invasive glucose monitoring in patients with Type 1 diabetes: a multisensor system combining sensors for dielectric and optical characterisation of skin. Biosens. Bioelectron. 24(9):2778–2784, 2009.

    Article  CAS  PubMed  Google Scholar 

  3. Chen, W., T. Kobayashi, S. Ichikawa, Y. Takeuchi, and T. Togawa. Continuous estimation of systolic blood pressure using the pulse arrival time and intermittent calibration. Med. Biol. Eng Comput. 38(5):569–574, 2000.

    Article  CAS  PubMed  Google Scholar 

  4. Chen, Y., C. Wen, G. Tao, and M. Bi. Continuous and noninvasive measurement of systolic and diastolic blood pressure by one mathematical model with the same model parameters and two separate pulse wave velocities. Ann. Biomed. Eng 40(4):871–882, 2012.

    Article  PubMed  Google Scholar 

  5. Custodio, V., F. J. Herrera, G. Lopez, and J. I. Moreno. A review on architectures and communications technologies for wearable health-monitoring systems. Sensors (Basel) 12(10):13907–13946, 2012. doi:10.3390/s121013907.

    Article  Google Scholar 

  6. Elliott, M., and A. Coventry. Critical care: the eight vital signs of patient monitoring. Br. J. Nurs. 21(10):621–625, 2012.

    Article  PubMed  Google Scholar 

  7. Handler, J. The importance of accurate blood pressure measurement. Perm. J. 13(3):51–54, 2009.

    PubMed Central  PubMed  Google Scholar 

  8. Haykin, S. Neural Networks A Comprehensive Foundation. New York: IEEE Press, 1994.

    Google Scholar 

  9. Martinez-Lozano, S. P., R. Zenobi, and M. Kohler. Analysis of the exhalome: a diagnostic tool of the future. Chest 144(3):746–749, 2013. doi:10.1378/chest.13-1106.

    Article  Google Scholar 

  10. Nair, P. Update on clinical inflammometry for the management of airway diseases. Can. Respir J. 20(2):117–120, 2013.

    PubMed Central  PubMed  Google Scholar 

  11. Pfaffe, T., J. Cooper-White, P. Beyerlein, K. Kostner, and C. Punyadeera. Diagnostic potential of saliva: current state and future applications. Clin. Chem. 57(5):675–687, 2011. doi:10.1373/clinchem.2010.153767.

    Article  CAS  PubMed  Google Scholar 

  12. Ramsay, M. A., M. Usman, E. Lagow, M. Mendoza, E. Untalan, and V. E. De. The accuracy, precision and reliability of measuring ventilatory rate and detecting ventilatory pause by rainbow acoustic monitoring and capnometry. Anesth. Analg. 117(1):69–75, 2013. doi:10.1213/ANE.0b013e318290c798.

    Article  PubMed  Google Scholar 

  13. Vashist, S. K. Non-invasive glucose monitoring technology in diabetes management: a review. Anal. Chim. Acta. 750:16–27, 2012. doi:10.1016/j.aca.2012.03.043.

    Article  CAS  PubMed  Google Scholar 

  14. Webster, J. G. Medical Instrumentation Application and Design (4th ed.). Hoboken: Wiley, 2009.

    Google Scholar 

  15. Yoon, Y., J. H. Cho, and G. Yoon. Non-constrained blood pressure monitoring using ECG and PPG for personal healthcare. J. Med. Syst. 33(4):261–266, 2009.

    Article  PubMed  Google Scholar 

  16. Zloczower, M., A. Z. Reznick, R. O. Zouby, and R. M. Nagler. Relationship of flow rate, uric acid, peroxidase, and superoxide dismutase activity levels with complications in diabetic patients: can saliva be used to diagnose diabetes? Antioxid. Redox Signal. 9(6):765–773, 2007.

    Article  CAS  PubMed  Google Scholar 

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Acknowledgments

We thank Sunny Smith, Charles Della Santina, and Lani Swarthout for material support, helpful discussions, and contribution to the logistics associated with prototype development and human studies. We thank Divya Kernik, Lucas Fridman, and Alexandra Della Santina for their contribution to the early prototype designs.

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Authors and Affiliations

  1. Department of Otolaryngology Head and Neck Surgery, Johns Hopkins University, Ross Bldg. 833, 720 Rutland Ave, Baltimore, MD, 21205, USA

    Gene Y. Fridman, Hai Tang & Yang Hong

  2. Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, 21205, USA

    Gene Y. Fridman

  3. Division of Pulmonary and Critical Care Medicine, Johns Hopkins University, Baltimore, MD, 21205, USA

    David Feller-Kopman

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  1. Gene Y. Fridman
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  2. Hai Tang
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  3. David Feller-Kopman
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  4. Yang Hong
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Correspondence to Gene Y. Fridman.

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Associate Editor James Tunnell oversaw the review of this article.

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Fridman, G.Y., Tang, H., Feller-Kopman, D. et al. MouthLab: A Tricorder Concept Optimized for Rapid Medical Assessment. Ann Biomed Eng 43, 2175–2184 (2015). https://doi.org/10.1007/s10439-015-1247-1

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  • DOI: https://doi.org/10.1007/s10439-015-1247-1

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