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

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Abstract

Title:: Crackle and Breathing Phase Detection in Lung Sounds with Deep Bidirectional Gated Recurrent Neural Networks.
Authors:: Messner E
Fediuk M
Swatek P
Scheidl S
Smolle-Juttner FM
Olschewski H
Pernkopf F
Source:: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference [Annu Int Conf IEEE Eng Med Biol Soc] 2018 Jul; Vol. 2018, pp. 356-359.
Publication Type:: Journal Article; Research Support, Non-U.S. Gov't
Language:: English
Imprint Name(s):: Original Publication: [Piscataway, NJ] : [IEEE], [2007]-
MeSH Terms:: Neural Networks, Computer*
Respiratory Sounds*/diagnosis
Heart Sounds ; Humans ; Lung ; Respiration ; Sound ; Sound Spectrography/methods
Entry Date(s):: Date Created: 20181117 Date Completed: 20190912 Latest Revision: 20200928
Update Code:: 20240105
DOI:: 10.1109/EMBC.2018.8512237
PMID:: 30440410
: Academic Journal

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 F 1 ≈ 86% for breathing phase events and F 1 ≈ 72% for crackles. The proposed method shows robustness regarding the contamination of the lung sound recordings with noise, bowel and heart sounds.

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