Abstract
The need of making improvements in obtaining (in a non-invasive way) and monitoring the breathing rate parameters in a patient emerges due to (1) the great amount of breathing problems our society suffer, (2) the problems that can be solved, and (3) the methods used so far. Non-specific machines are usually used to carry out these measures or simply calculate the number of inhalations and exhalations within a particular timeframe. These methods lack of effectiveness and precision thus, influencing the capacity of getting a good diagnosis. This proposal focuses on drawing up a technology composed of a mechanism and a user application which allows doctors to obtain the breathing rate parameters in a comfortable and concise way. In addition, such parameters are stored in a database for potential consultation as well as for the medical history of the patients. For this, the current approach takes into account the needs, the capacities, the expectations and the user motivations which have been compiled by means of open interviews, forum discussions, surveys and application uses. In addition, an empirical evaluation has been conducted with a set of volunteers. Results indicate that the proposed technology may reduce cost and improve the reliability of the diagnosis.
The original version of this chapter was revised: The Acknowledgements section was included. The erratum to this chapter is available at https://doi.org/10.1007/978-3-319-56154-7_65
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
OMS|Enfermedad pulmonar obstructiva crónica (EPOC). WHO. http://www.who.int/respiratory/copd/es/. Accessed 09 June 2016
SEPAR NP Morbilidad hospitalaria neumología.pdf, Google Docs, 5 ene 2016. https://drive.google.com/file/d/0B3-GelWPMn4dNjdCUThzMS00MkE. Accessed 09 June 2016
SEPAR_NP La educacion de los profesionales mejorará el diagnosttico de la FPI.pdf, 2 November 2015, Google Docs. https://drive.google.com/file/d/0B3-GelWPMn4dUi02TVhHb2NPbnM/view?pref=2&pli=1&usp=embed_facebook. Accessed 09 June 2016
Frecuencia respiratoria normal|Salud y bienestar. http://lasaludi.info/frecuencia-respiratoria-normal.html. Accessed 21 Jan 2016
Fieselmann, J.F., Hendryx, M.S., Helms, C.M., Wakefield, D.S.: Respiratory rate predicts cardiopulmonary arrest for internal medicine inpatients. J. Gen. Intern. Med. 8(7), 354–360 (1993)
Subbe, C.P., Davies, R.G., Williams, E., Rutherford, P., Gemmell, L.: Effect of introducing the modified early warning score on clinical outcomes, cardio-pulmonary arrests and intensive care utilisation in acute medical admissions. Anaesthesia 58(8), 797–802 (2003)
Goldhill, D.R., McNarry, A.F., Mandersloot, G., McGinley, A.: A physiologically-based early warning score for ward patients: the association between score and outcome. Anaesthesia 60(6), 547–553 (2005)
Cretikos, M., et al.: The objective medical emergency team activation criteria: a case-control study. Resuscitation 73(1), 62–72 (2007)
Goldhill, D.R., McNarry, A.F.: Physiological abnormalities in early warning scores are related to mortality in adult inpatients. Br. J. Anaesth. 92(6), 882–884 (2004)
Cook, C.J., Smith, G.B.: Do textbooks of clinical examination contain information regarding the assessment of critically ill patients? Resuscitation 60(2), 129–136 (2004)
Hassan Montero, Y., Ortega Santamaría, S.: Informe APEI sobre usabilidad
Arduino - ArduinoBoardNano. https://www.arduino.cc/en/Main/ArduinoBoardNano. Accessed 09 June 2016
Arduino - Software. https://www.arduino.cc/en/Main/Software#. Accessed 09 June 2016
Processing. MIT Press. https://mitpress.mit.edu/books/processing-0. Accessed 04 Jan 2017
Webster, L.: Any Developer, Any App, Any Platform, Visual Studio, 16 November 2016. https://www.visualstudio.com/es/. Accessed 04 Jan 2017
Muñoz, V.J.E.: El nuevo PHP. Conceptos avanzados, Vicente Javier Eslava Muñoz (2013)
Reyes, B.A., Reljin, N., Chon, K.H.: Tracheal sounds acquisition using smartphones. Sensors 14(8), 13830–13850 (2014)
Nam, Y., Reyes, B.A., Chon, K.H.: Estimation of respiratory rates using the built-in microphone of a smartphone or headset. IEEE J. Biomed. Health Inform. 20(6), 1493–1501 (2016)
DICOM Homepage. http://dicom.nema.org/. Accessed 09 June 2016
Acknowledgements
This research has been supported by the Pololas project (TIN2016-76956-C3-2-R) and by the SoftPLM Network (TIN2015-71938-REDT) of the Spanish the Ministry of Economy and Competitiveness.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Morales, L., Morales, M.D., Jiménez-Ramírez, A., Escalona, M.J. (2017). A Microcontroller Based System for Controlling Patient Respiratory Guidelines. In: Rojas, I., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2017. Lecture Notes in Computer Science(), vol 10209. Springer, Cham. https://doi.org/10.1007/978-3-319-56154-7_56
Download citation
DOI: https://doi.org/10.1007/978-3-319-56154-7_56
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-56153-0
Online ISBN: 978-3-319-56154-7
eBook Packages: Computer ScienceComputer Science (R0)