# Convolutional neural network for breathing phase detection in lung sounds

arXiv: https://arxiv.org/abs/1903.10251 · HTML: https://mlsanity.com/paper/1903.10251/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Mar 2019.

## Built on by (most cited first, 8 of 8)

- [Benchmarking of eight recurrent neural network variants for breath phase and adventitious sound detection on a self-developed open-access lung sound database-HF_Lung_V1](https://mlsanity.com/paper/2102.03049/) (2021)
- [Development of a Respiratory Sound Labeling Software for Training a Deep Learning-Based Respiratory Sound Analysis Model](https://mlsanity.com/paper/2101.01352/) (2021)
- [An Update on a Progressively Expanded Database for Automated Lung Sound Analysis](https://mlsanity.com/paper/2102.04062/) (2021)
- [Crackle Detection In Lung Sounds Using Transfer Learning And Multi-Input Convolitional Neural Networks](https://mlsanity.com/paper/2104.14921/) (2021)
- [Robust and Interpretable Temporal Convolution Network for Event Detection in Lung Sound Recordings](https://mlsanity.com/paper/2106.15835/) (2021)
- [Detecting Respiratory Pathologies Using Convolutional Neural Networks and Variational Autoencoders for Unbalancing Data](https://mlsanity.com/paper/2402.02183/) (2024)
- [A Dual-Purpose Deep Learning Model for Auscultated Lung and Tracheal Sound Analysis Based on Mixed Set Training](https://mlsanity.com/paper/2107.04229/) (2021)
- [EZhouNet:A framework based on graph neural network and anchor interval for the respiratory sound event detection](https://mlsanity.com/paper/2509.01153/) (2025)

## Abstract (arXiv)

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.