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

*Korrespondierende/r Autor/-in für diese Arbeit

Publikation: Beitrag in einer FachzeitschriftArtikelBegutachtung


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.

FachzeitschriftComputers in Biology and Medicine
PublikationsstatusVeröffentlicht - 2020

ASJC Scopus subject areas

  • Gesundheitsinformatik
  • Angewandte Informatik

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