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

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem Konferenzband

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.

Originalspracheenglisch
Titel40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018
Herausgeber (Verlag)Institute of Electrical and Electronics Engineers
Seiten356-359
Seitenumfang4
Band2018-July
ISBN (elektronisch)9781538636466
DOIs
PublikationsstatusVeröffentlicht - 26 Okt 2018
Veranstaltung40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018 - Honolulu, USA / Vereinigte Staaten
Dauer: 18 Jul 201821 Jul 2018

Konferenz

Konferenz40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018
LandUSA / Vereinigte Staaten
OrtHonolulu
Zeitraum18/07/1821/07/18

ASJC Scopus subject areas

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

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