Exploring Model Architectures for Real-Time Lung Sound Event Detection

Abstract

Computerized detection of relevant lung sound events has the potential to assist physicians during auscultation and to monitor the severity of pulmonary diseases in ambulatory settings. In some cases, real-time detection of adventitious lung sounds is required to provide instant feedback to physicians, e.g. during autogenic drainage therapy. State-of-the-art solutions for this task leverage deep learning models, which vary significantly in complexity. For real-time applications on resource-constrained devices, such as stethoscope-integrated hardware, both detection accuracy and model complexity are important to consider. While most existing research focusses primarily on accuracy, this work evaluates both accuracy and computational complexity. The contributions of this work are threefold. First, the effect of using a full breathing cycle as input is studied to assess its impact on event detection performance. This approach introduces a computational cost due to the required segmentation process. Second, a transformer-based architecture is compared with two relatively simple convolutional models, each utilizing different input horizons. Evaluations are conducted on both public and in-house lung sound datasets. Third, recognizing that the event detection task aligns better with a multi-label setting than the commonly used multi-class setup, this study compares both approaches. We conclude that a multi-label output outperforms a multi-class approach, that inputs segmented per breathing cycle are preferred, and that the high complexity models have similar performance to the models with low complexity on unseen data.