Estimation of Respiratory Rate from Breathing Audio

Abstract

The COVID-19 pandemic has fueled exponential growth in the adoption of remote delivery of primary, specialty, and urgent health care services. One major challenge is the lack of access to physical exam including accurate and inexpensive measurement of remote vital signs. Here we present a novel method for machine learning-based estimation of patient respiratory rate from audio. There exist non-learning methods but their accuracy is limited and work using machine learning known to us is either not directly useful or uses non-public datasets. We are aware of only one publicly available dataset which is small and which we use to evaluate our algorithm. However, to avoid the overfitting problem, we expand its effective size by proposing a new data augmentation method. Our algorithm uses the spectrogram representation and requires labels for breathing cycles, which are used to train a recurrent neural network for recognizing the cycles. Our augmentation method exploits the independence property of the most periodic frequency components of the spectrogram and permutes their order to create multiple signal representations. Our experiments show that our method almost halves the errors obtained by the existing (non-learning) methods.Clinical relevance-We achieve a Mean Absolute Error (MAE) of 1.0 for the respiratory rate while relying only on an audio signal of a patient breathing. This signal can be collected from a smartphone such that physicians can automatically and reliably determine respiratory rate in a remote setting.

Respiratory Rate Estimation during Walking and Running Using Breathing Sounds Recorded with a Microphone

“…Anecdotally, endurance athletes monitor their opponents’ breathing sounds to gauge their physical effort during competitions [ 2 ], and microphones could be used to capture this valuable information. Microphones can easily be integrated into facemasks or other devices, and the sound signal can be processed for extracting f R [ 18 , 19 , 20 , 21 ]. There are different types of microphones that employ a variety of methods to convert the air pressure variations of a sound wave to an electrical signal.…”