Monitoring of prevalent airborne diseases such as COVID-19 characteristically involve respiratory assessments. While auscultation is a mainstream method for symptomatic monitoring, its diagnostic utility is hampered by the need for dedicated hospital visits. Continual remote monitoring based on recordings of respiratory sounds on portable devices is a promising alternative, which can assist in screening of COVID-19. 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 spectrogram transformer (HST) on spectrogram representations of respiratory sounds. HST embodies self-attention mechanisms 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. Comprehensive demonstrations on a multi-national dataset indicate that HST outperforms competing methods, achieving over 97% area under the receiver operating characteristic curve (AUC) in detecting COVID-19 cases.

翻译：对流行性空气传播疾病的监测,如COVID-19(COVID-19)通常涉及呼吸系统评估。虽然培养是表征监测的一种主流方法,但其诊断效用因需要专门进行医院检查而受阻。基于便携式装置上呼吸声录音的连续遥控监测是一种有希望的替代办法,有助于筛查COVID-19。在这项研究中,我们采用了一种新的深层次学习方法,将COVID-19(COVID-19)患者与有咳嗽或呼吸声录音记录的健康控制区分开来。拟议方法在呼吸道声音的光谱显示方面利用一种新的等级分光谱变压器。HST(HST)对当地视窗安装了自留机制,窗口大小逐渐跨越模型阶段,以捕捉本地到全球的情况。HST与最先进的常规和深学习基线进行比较。关于多国数据集的全面演示表明,HST超越了相互竞争的方法,在检测COVID-19案例时在接收器操作特征曲线下达到97%以上的区域。