Estimation of respiratory rate and exhale duration using audio signals recorded by smartphone microphones

•Audio respiratory data recorded remotely by 210 volunteers (52 COVID-19).•A method to classify audio signal quality for respiratory monitoring is presented.•Mean absolute error of respiratory rate is 0.2 bpm during laboratory tests.•Mean absolute error of respiratory rate is 0.79 bpm during remote recordings.•Audio exhale duration error 0.21 s vs respiratory inductance plethysmography. Monitoring respiration using mobile technology has potential to contribute to the clinical management of patients with infectious diseases or chronic respiratory conditions in the home. In this study, a new method to estimate respiratory rate and exhale duration from audio data recorded using smartphone microphones was developed. The method first determines the fundamental frequency of the audio signal, which guides an adaptive thresholding method to detect individual exhales. Exhale boundary times were refined using adaptive physiological thresholds. To control for environmental noise in remote audio recordings, a method to classify audio signals as acceptable or unacceptable for accurate respiration monitoring was developed. Estimated respiratory rates and audio exhale durations were validated against respiratory inductance plethysmography (RIP) in 27 healthy participants. A further 217 audio recordings were collected remotely by 210 healthy and COVID-19 participants, with results compared with researcher annotations. Compared to RIP in the laboratory, respiratory rate was estimated with a mean absolute error (MAE) of 0.2 ± 0.27 bpm, and within 1 bpm for 96 % of recordings (r = 0.99). Compared to researcher annotations for remote recordings, respiratory rate was estimated with a MAE of 0.79 ± 2.44 bpm, and within 1 bpm for 87.5 % of recordings (r = 0.92), while audio exhale duration was estimated with a MAE of 0.21 ± 0.23 s. Audio signal quality was classified with an area under the receiver operating characteristic of 0.81 ±. This method offers the potential for accurate remote monitoring of respiratory rate and breathing patterns in individuals with COVID-19 and other chronic respiratory conditions.