OFGST-Swin: Swin Transformer utilizing Overlap Fusion-based Generalized S-Transform for Respiratory Cycle Classification

Respiratory diseases pose a massive threat to human health, thus early diagnosis and treatment are essential. Although electronic stethoscopes have shown effectiveness in enhancing auscultation, the diagnosis still necessitates the expertise of a specialist. In this paper, we propose a Swin Transformer utilizing Overlap Fusion-based Generalized S-Transform (OFGST-Swin) for respiratory cycle classification. The proposed OFGST-Swin demonstrates the capability to categorize respiratory sounds captured by electronic stethoscopes and detect adventitious respiratory cycles within these recordings, and it consists of two novel modules: the Sliding Windows-based Augmentation (SWA) for respiratory cycle data enhancement, and the Overlap Fusion-based Generalized S-Transform (OFGST) for respiratory cycle feature extraction. The SWA addresses data imbalance in medical datasets by generating adventitious respiratory cycles through a sliding window. The OFGST incorporates the innovative Triangular Window-based Overlap Fusion (TWOF) into the Enhanced Generalized S-Transform (EGST), for extracting respiratory cycle features. The proposed OFGST-Swin has been evaluated on two datasets, the ICBHI 2017 dataset and the SPRsound respiratory sound dataset. The experimental results indicate that the proposed OFGST-Swin achieves a better accuracy score of 0.5605 on four-categories classification tasks in the ICBHI 2017 dataset, and 0.8018 on seven-categories classification tasks in the SPRsound dataset. The proposed method, serving as a signal processing backend for electronic stethoscopes, offers highly effective diagnostic advice to physicians.