OFGST-Swin: Swin Transformer Utilizing Overlap Fusion-Based Generalized S-Transform for Respiratory Cycle Classification

• Published in: IEEE transactions on instrumentation and measurement 2024, Vol.73, p.1-13
• Main Authors: Wang, Fan, Yuan, Xiaochen, Bao, Junqi, Lam, Chan-Tong, Huang, Guoheng, Chen, Hai
• Format: Article
• Language: English
• Subjects: Classification; Convolutional neural networks; Datasets; Diagnosis; Effectiveness; Enhanced generalized S-transform (EGST); Feature extraction; Pulmonary diseases; respiratory cycle classification; Respiratory diseases; Signal classification; Sliding; sliding window-based augmentation (SWA); Spectrogram; Stethoscope; Stethoscopes; Task analysis; Transformations (mathematics); Transformers
• ISSN: 0018-9456; 1557-9662
• DOI: 10.1109/TIM.2024.3428637

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 article, 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 window-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-category classification tasks in the ICBHI 2017 dataset, and 0.8018 on seven-category 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.