Intelligent Stethoscope using Full Self-Attention Mechanism for Abnormal Respiratory Sound Recognition

Changyi Wu1; Dongmin Huang1; Xiaoting Tao2; Kun Qiao2; Hongzhou Lu2; Wenjin Wang1

10.1109/BHI58575.2023.10313454

2023

2641-3590

979-8-3503-1051-1

2023 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI)

1-4

15-18 Oct. 2023

Pittsburgh, PA, USA

Machine learning automates the recognition of abnormal respiratory sounds and pulmonary diseases for wireless stethoscopes. However, most learning-based methods have unbalanced performance between low sensitivity (SEN) and high specificity (SPE). Recently, the full self-attention mechanism-based Transformer made significant progress in various medical tasks, but its role in respiratory sound recognition still remains unknown. It can extract the contextual information from segments with arbitrary length in a signal, especially with long-range dependencies. This is typically suitable for mining the pattern of temporally-continuous pathological respiratory sounds, including stridor, wheezes, and rhonchi. Thus in this paper, we explore the feasibility of using full self-attention mechanism of Audio Spectrogram Transformer (AST) to improve the performance of respiratory sound recognition, where FNN, CNN and AST are benchmarked on the dataset of ICBHI 2017. In our proposed framework, the input samples are generated by a new respiratory cycle-based segmentation in order to preserve the consistency of input representation; a dual-input AST model is designed to enhance the robustness to disturbances by extracting the complementary information between the spectrograms and log Mel spectrograms. Extensive experiments show that AST outperforms other methods in the task of respiratory sound recognition. Moreover, the proposed respiratory cycle-based segmentation considerably improves SEN by almost 10%.

Stethoscope respiratory sound Transformer signal segmentation ICBHI 2017

第一

CPCI-S ; EI

WOS:001107519300043

20235015211200

Spectrographs

Optical Devices and Systems:741.3 ; Acoustic Waves:751.1

IEEE

1.Department of Biomedical Engineering, Southern University of Science and Technology, China
2.Department of Thoracic Surgery, The Third People’s Hospital of Shenzhen, China

Changyi Wu,Dongmin Huang,Xiaoting Tao,et al. Intelligent Stethoscope using Full Self-Attention Mechanism for Abnormal Respiratory Sound Recognition[C],2023:1-4.