Multi-channel lung sound classification with convolutional recurrent neural networks
By Elmar Messner, M. Fediuk, P. Swatek, S. Scheidl, F. Smolle-Jüttner, H. Olschewski, Franz Pernkopf. 2020
76 citations recorded by the source corpus.
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.
Subfields: Pulmonary and Respiratory Medicine, Signal Processing
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In Pulmonary and Respiratory Medicine
In Signal Processing
- Eduardo Fonseca
- Xavier Serra
- Frederic Font
- Xavier Favory
- Jordi Pons
- Andrés Ferraro
- Jakob Abeßer
- D. Ellis
Citations in the corpus
- FSD50K: An Open Dataset of Human-Labeled Sound Events (2020, background): “1 SER has gained increasing attention in the past few years, becoming a key component in applications related to healthcare [1]–[3], urban sound planning [4], bioacoustics monitoring [5]–[7], multimedia event detection [8], large-scale event discovery [9], surveillance [10, 11], or noise monitoring…”