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