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Article

Convolutional Neural Network for Breathing Phase Detection in Lung Sounds

1
CINTESIS-Center for Health Technologies and Information Systems Research, Faculty of Medicine, University of Porto, 4200-450 Porto, Portugal
2
Medsensio AS, N-9037 Tromsø, Norway
3
Department of Computer Science, UiT The Arctic University of Norway, N-9037 Tromsø, Norway
4
General Practice Research Unit in Tromsø, Department of Community Medicine, UiT The Arctic University of Norway, N-9037 Tromsø, Norway
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Sensors 2019, 19(8), 1798; https://doi.org/10.3390/s19081798
Received: 24 March 2019 / Revised: 12 April 2019 / Accepted: 13 April 2019 / Published: 15 April 2019
(This article belongs to the Section Biosensors)
We applied deep learning to create an algorithm for breathing phase detection in lung sound recordings, and we compared the breathing phases detected by the algorithm and manually annotated by two experienced lung sound researchers. Our algorithm uses a convolutional neural network with spectrograms as the features, removing the need to specify features explicitly. We trained and evaluated the algorithm using three subsets that are larger than previously seen in the literature. We evaluated the performance of the method using two methods. First, discrete count of agreed breathing phases (using 50% overlap between a pair of boxes), shows a mean agreement with lung sound experts of 97% for inspiration and 87% for expiration. Second, the fraction of time of agreement (in seconds) gives higher pseudo-kappa values for inspiration (0.73–0.88) than expiration (0.63–0.84), showing an average sensitivity of 97% and an average specificity of 84%. With both evaluation methods, the agreement between the annotators and the algorithm shows human level performance for the algorithm. The developed algorithm is valid for detecting breathing phases in lung sound recordings. View Full-Text
Keywords: respiratory phases; breath onset; breath detection; spectrograms; automated classification; deep learning respiratory phases; breath onset; breath detection; spectrograms; automated classification; deep learning
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MDPI and ACS Style

Jácome, C.; Ravn, J.; Holsbø, E.; Aviles-Solis, J.C.; Melbye, H.; Ailo Bongo, L. Convolutional Neural Network for Breathing Phase Detection in Lung Sounds. Sensors 2019, 19, 1798. https://doi.org/10.3390/s19081798

AMA Style

Jácome C, Ravn J, Holsbø E, Aviles-Solis JC, Melbye H, Ailo Bongo L. Convolutional Neural Network for Breathing Phase Detection in Lung Sounds. Sensors. 2019; 19(8):1798. https://doi.org/10.3390/s19081798

Chicago/Turabian Style

Jácome, Cristina; Ravn, Johan; Holsbø, Einar; Aviles-Solis, Juan C.; Melbye, Hasse; Ailo Bongo, Lars. 2019. "Convolutional Neural Network for Breathing Phase Detection in Lung Sounds" Sensors 19, no. 8: 1798. https://doi.org/10.3390/s19081798

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