COVID-19 Detection from respiratory sounds with hierarchical spectrogram transformers

Abstract

Monitoring of prevalent airborne diseases such as COVID-19 characteristically involves respiratory assessments. While auscultation is a mainstream method for preliminary screening of disease symptoms, its util ity is hampered by the need for dedicated hospital visits. Remote monitoring based on recordings of respi ratory sounds on portable devices is a promising alter native, which can assist in early assessment of COVID-19 that primarily affects the lower respiratory tract. In this study, we introduce a novel deep learning approach to distinguish patients with COVID-19 from healthy controls given audio recordings of cough or breathing sounds. The proposed approach leverages a novel hierarchical spectro gram transformer (HST) on spectrogram representations of respiratory sounds. HST embodies self-attention mech anisms over local windows in spectrograms, and window size is progressively grown over model stages to capture local to global context. HST is compared against state-of the-art conventional and deep-learning baselines. Demon strations on crowd-sourced multi-national datasets indicate that HST outperforms competing methods, achieving over 90% area under the receiver operating characteristic curve (AUC) in detecting COVID-19 cases.