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Messner, E; Fediuk, M; Swatek, P; Scheidl, S; Smolle-Jüttner, FM; Olschewski, H; Pernkopf, F.
Multi-channel lung sound classification with convolutional recurrent neural networks.
Comput Biol Med. 2020; 122(4):103831-103831 Doi: 10.1016/j.compbiomed.2020.103831
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Führende Autor*innen der Med Uni Graz
Messner Elmar
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 an approach for multi-channel lung sound classification, exploiting spectral, temporal and spatial information. In particular, we propose a frame-wise classification framework to process full breathing cycles of multi-channel lung sound recordings with a convolutional recurrent neural network. With our recently developed 16-channel lung sound recording device, we collect lung sound recordings from lung-healthy subjects and patients with idiopathic pulmonary fibrosis (IPF), within a clinical trial. From the lung sound recordings, we extract spectrogram features and compare different deep neural network architectures for binary classification, i.e. healthy vs. pathological. Our proposed classification framework with the convolutional recurrent neural network outperforms the other networks by achieving an F-score of F1≈92%. Together with our multi-channel lung sound recording device, we present a holistic approach to multi-channel lung sound analysis. Copyright © 2020 Elsevier Ltd. All rights reserved.

Find related publications in this database (Keywords)
Multi-channel lung sound classification
Deep learning
Convolutional recurrent neural networks
Pulmonary fibrosis
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