Adolescent Asthma Monitoring: A Preliminary Study of Audio and Spirometry Modalities

Asthma patients' sleep quality is correlated with how well their asthma symptoms are controlled. In this paper, deep learning techniques are explored to improve forecasting of forced expiratory volume in one second (FEV1) by using audio data from participants and test whether auditory sleep disturbances are correlated with poorer asthma outcomes. These are applied to a representative data set of FEV1 collected from a commercially available sprirometer and audio spectrograms collected overnight using a smartphone. A model for detecting nonverbal vocalizations including coughs, sneezes, sighs, snoring, throat clearing, sniffs, and breathing sounds was trained and used to capture nightly sleep disturbances. Our preliminary analysis found significant improvement in FEV1 forecasting when using overnight nonverbal vocalization detections as an additional feature for regression using XGBoost over using only spirometry data. Clinical relevance- This preliminary study establishes up to 30% improvement of FEV1 forecasting using features generated by deep learning techniques over only spirometry-based features.