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 language | English |
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Title of host publication | 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018 |
Publisher | Institute of Electrical and Electronics Engineers |
Pages | 356-359 |
Number of pages | 4 |
Volume | 2018-July |
ISBN (Electronic) | 9781538636466 |
DOIs | |
Publication status | Published - 26 Oct 2018 |
Event | 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society: EMBC 2018 - Honolulu, United States Duration: 18 Jul 2018 → 21 Jul 2018 |
Conference
Conference | 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society |
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Country/Territory | United States |
City | Honolulu |
Period | 18/07/18 → 21/07/18 |
ASJC Scopus subject areas
- Signal Processing
- Biomedical Engineering
- Computer Vision and Pattern Recognition
- Health Informatics