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Automatic Analysis of Lung Function Based on Smartphone Recordings

  • João F. TeixeiraEmail author
  • Luís F. Teixeira
  • João Fonseca
  • Tiago Jacinto
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 574)

Abstract

Over 250 million people, worldwide, are affected by chronic lung conditions such as Asthma and COPD. These can cause breathlessness, a harsh decrease in quality of life and, if left undetected or not properly managed, even death. In this paper, we approached part of the lines of development suggested upon earlier work. This concerned the development of a system design for a smartphone lung function classification app, which would only use recordings from the built-in microphone. A more systematic method to evaluate the relevant combinations of methods was devised and an additional set of 44 recordings was used for testing purposes. The previous 101 were kept for training the models. The results enabled to further reduce the signal processing pipeline leading to the use of 6 envelopes, per recording, half of the previous amount. An analysis of the classification performances is provided for both previous tasks: differentiation into Normal from Abnormal lung function, and between multiple lung function patterns. The results from this project encourage further development of the system.

Keywords

Asthma Breath COPD Machine learning Signal processing Smartphone Spirometry 

Notes

Acknowledgements

This work was conducted with the support of the Control and Burden of Asthma and Rhinitis project (ICAR), with the grant PTDC/SAU-SAP/119192/ 2010. The authors would like to thank Bernardo Pinho for the development of an enhanced recording app and Ivânia Gonçalves, Rita Silva and Daniela Santos for the added effort of recording the patients in addition to their tasks on the patient screenings.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • João F. Teixeira
    • 1
    Email author
  • Luís F. Teixeira
    • 2
  • João Fonseca
    • 3
  • Tiago Jacinto
    • 3
  1. 1.Department of Electrical and Computer EngineeringUniversity of PortoPortoPortugal
  2. 2.Department of Informatics EngineeringUniversity of PortoPortoPortugal
  3. 3.Department of Health Information and Decision SciencesUniversity of PortoPortoPortugal

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