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Messner, E; Fediuk, M; Swatek, P; Scheidl, S; Smolle-Juttner, FM; Olschewski, H; Pernkopf, F.
Crackle and Breathing Phase Detection in Lung Sounds with Deep Bidirectional Gated Recurrent Neural Networks.
Conf Proc IEEE Eng Med Biol Soc. 2018; 2018: 356-359. Doi: 10.1109/EMBC.2018.8512237
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Co-Autor*innen der Med Uni Graz
Fediuk Melanie
Olschewski Horst
Scheidl Stefan
Smolle-Juettner Freyja-Maria
Swatek Paul

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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.

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