ArticleSensors (Basel, Switzerland)2019
Convolutional Neural Network for Breathing Phase Detection in Lung Sounds.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Lightweight Hybrid Deep Learning Models for Accurate Classification of Respiratory Conditions from Raw Lung Sounds.Journal of medical systems · 2025Article
- Real-time airway monitoring system using binary classification model based on respiratory sounds of rabbits with a tracheostomy tube.Scientific reports · 2025Article
- Automated system for diagnosing pulmonary fibrosis using crackle analysis in recorded lung sounds based on iterative envelope mean fractal dimension filter.Physiological measurement · 2025Article
- Multimodal Respiratory Rate Estimation From Audio and Video in Emergency Department Patients.IEEE journal of translational engineering in health and medicine · 2025Article
- Machines Are Learning Chest Auscultation. Will They Also Become Our Teachers?CHEST pulmonary · 2024Review
- Enabling Continuous Breathing-Phase Contextualization via Wearable-Based Impedance Pneumography and Lung Sounds: A Feasibility Study.IEEE journal of biomedical and health informatics · 2023Article
- Pulmonary disease detection and classification in patient respiratory audio files using long short-term memory neural networks.Frontiers in medicine · 2023Article
- Enabling effective breathing sound analysis for automated diagnosis of lung diseases.Smart health (Amsterdam, Netherlands) · 2022Article
- Deep learning models for detecting respiratory pathologies from raw lung auscultation sounds.Soft computing · 2022Article
- Article
- Detecting Respiratory Pathologies Using Convolutional Neural Networks and Variational Autoencoders for Unbalancing Data.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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