In Computers in biology and medicine
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.
Messner Elmar, Fediuk Melanie, Swatek Paul, Scheidl Stefan, Smolle-Jüttner Freyja-Maria, Olschewski Horst, Pernkopf Franz
Auscultation, Convolutional recurrent neural networks, Deep learning, Multi-channel lung sound classification, Pulmonary fibrosis