Respiratory airflow estimation from lung sounds based on regression

Elmar Messner, Martin Hagmuller, Paul Swatek, Freyja Maria Smolle-Juttner, Franz Pernkopf

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

Abstract

The aim of this work is the estimation of respiratory flow from lung sound recordings, i.e. acoustic airflow estimation. With a 16-channel lung sound recording device, we simultaneously record the respiratory flow and the lung sounds on the posterior chest from six lung-healthy subjects in supine position. For the recordings of four selected sensor positions, we extract linear frequency cepstral coefficient (LFCC) features and map these on the airflow signal. We use multivariate polynomial regression to fit the features to the airflow signal. Compared to most of the previous approaches, the proposed method uses lung sounds instead of trachea sounds. Furthermore, our method masters the estimation of the airflow without prior knowledge of the respiratory phase, i.e. no additional algorithm for phase detection is required. Another benefit is the avoidance of time-consuming calibration. In experiments, we evaluate the proposed method for various selections of sensor positions in terms of mean squared error (MSE) between estimated and actual airflow. Moreover, we show the accuracy of the method regarding a frame-based breathing-phase detection.

Original languageEnglish
Title of host publication2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers
Pages1123-1127
Number of pages5
ISBN (Electronic)9781509041176
DOIs
Publication statusPublished - 16 Jun 2017
Event2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - New Orleans, United States
Duration: 5 Mar 20179 Mar 2017

Conference

Conference2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017
CountryUnited States
CityNew Orleans
Period5/03/179/03/17

Keywords

  • acoustic airflow estimation
  • linear frequency cepstral coefficients (LFCCs)
  • lung sounds
  • multichannel recording device
  • multivariate polynomial regression

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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  • Cite this

    Messner, E., Hagmuller, M., Swatek, P., Smolle-Juttner, F. M., & Pernkopf, F. (2017). Respiratory airflow estimation from lung sounds based on regression. In 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings (pp. 1123-1127). [7952331] Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICASSP.2017.7952331