Crackle and Breathing Phase Detection in Lung Sounds with Deep Bidirectional Gated Recurrent Neural Networks

Elmar Messner, Melanie Fediuk, Paul Swatek, Stefan Scheidl, Freyja Maria Smolle-Juttner, Horst Olschewski, Franz Pernkopf

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we present a method for event detection in single-channel lung sound recordings. This includes the detection of crackles and breathing phase events (inspiration/expiration). Therefore, we propose an event detection approach with spectral features and bidirectional gated recurrent neural networks (BiGRNNs). In our experiments, we use multichannel lung sound recordings from lung-healthy subjects and patients diagnosed with idiopathic pulmonary fibrosis, collected within a clinical trial. We achieve an event-based F-score of F1 ≈ 86% for breathing phase events and F1 ≈ 72% for crackles. The proposed method shows robustness regarding the contamination of the lung sound recordings with noise, bowel and heart sounds.

Original languageEnglish
Title of host publication40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018
PublisherInstitute of Electrical and Electronics Engineers
Pages356-359
Number of pages4
Volume2018-July
ISBN (Electronic)9781538636466
DOIs
Publication statusPublished - 26 Oct 2018
Event40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018 - Honolulu, United States
Duration: 18 Jul 201821 Jul 2018

Conference

Conference40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018
CountryUnited States
CityHonolulu
Period18/07/1821/07/18

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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  • Cite this

    Messner, E., Fediuk, M., Swatek, P., Scheidl, S., Smolle-Juttner, F. M., Olschewski, H., & Pernkopf, F. (2018). Crackle and Breathing Phase Detection in Lung Sounds with Deep Bidirectional Gated Recurrent Neural Networks. In 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018 (Vol. 2018-July, pp. 356-359). [8512237] Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/EMBC.2018.8512237