Multi-channel Lung Sound Classification with Convolutional Recurrent Neural Networks

Elmar Messner*, Melanie Fediuk, Paul Swatek, Stefan Scheidl, Maria Smolle-Jüttner, Horst Olschewski, Franz Pernkopf

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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 F 1≈92%. Together with our multi-channel lung sound recording device, we present a holistic approach to multi-channel lung sound analysis.

Original languageEnglish
Article number103831
Number of pages10
JournalComputers in Biology and Medicine
Publication statusPublished - 2020


  • Auscultation
  • Convolutional recurrent neural networks
  • Deep learning
  • Multi-channel lung sound classification
  • Pulmonary fibrosis

ASJC Scopus subject areas

  • Health Informatics
  • Computer Science Applications

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