TY - JOUR
T1 - OFGST-Swin
T2 - Swin Transformer Utilizing Overlap Fusion-Based Generalized S-Transform for Respiratory Cycle Classification
AU - Wang, Fan
AU - Yuan, Xiaochen
AU - Bao, Junqi
AU - Lam, Chan Tong
AU - Huang, Guoheng
AU - Chen, Hai
N1 - Publisher Copyright:
© 1963-2012 IEEE.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - Enhanced generalized S-transform (EGST)
KW - respiratory cycle classification
KW - sliding window-based augmentation (SWA)
UR - http://www.scopus.com/inward/record.url?scp=85199107899&partnerID=8YFLogxK
U2 - 10.1109/TIM.2024.3428637
DO - 10.1109/TIM.2024.3428637
M3 - Article
AN - SCOPUS:85199107899
SN - 0018-9456
VL - 73
JO - IEEE Transactions on Instrumentation and Measurement
JF - IEEE Transactions on Instrumentation and Measurement
M1 - 2525913
ER -