A dual-purpose deep learning model for auscultated lung and tracheal sound analysis based on mixed set training
Journal
Biomedical Signal Processing and Control
Journal Volume
86
Date Issued
2023-09-01
Author(s)
Hsu, Fu Shun
Huang, Shang Ran
Su, Chang Fu
Huang, Chien Wen
Cheng, Yuan Ren
Chen, Chun Chieh
Chen, Chung Wei
Lai, Yen Chun
Cheng, Tang Wei
Lin, Nian Jhen
Tsai, Wan Ling
Lu, Ching Shiang
Chen, Chuan
Abstract
Many deep learning–based computerized respiratory sound analysis methods have previously been developed. However, these studies focus on either lung sound only or tracheal sound only. The effectiveness of using a lung sound analysis algorithm on tracheal sound and vice versa is rarely reported. Furthermore, no one knows whether using lung and tracheal sounds together in training a deep learning-based respiratory sound analysis model is beneficial. In this study, we first constructed a tracheal sound database, HF_Tracheal_V1, containing 10,448 15-s tracheal sound recordings, 21,741 inhalation labels, 15,858 exhalation labels, and 6414 continuous adventitious sound (CAS) labels collected from 227 participants undergoing a diagnostic/surgical procedure under monitored anesthesia care. HF_Tracheal_V1 and our previously built lung sound database, HF_Lung_V2, were either combined (mixed set), used one after the other (domain adaptation), or used alone to train convolutional neural network bidirectional gate recurrent unit models for inhalation, exhalation, and CAS detection in lung and tracheal sounds. The results revealed that the models trained using lung sound alone performed poorly in tracheal sound analysis and vice versa. However, mixed set training or domain adaptation improved the performance for 1) inhalation and exhalation detection in lung sounds and 2) inhalation, exhalation, and CAS detection in tracheal sounds compared to positive controls (the models trained using lung sound alone and used in lung sound analysis and vice versa). In particular, the model trained on the mixed set had great flexibility to serve two purposes, lung and tracheal sound analyses, at the same time.
Subjects
Convolutional neural network | Deep learning | Domain adaptation | Gated-recurrent unit | Lung sound | Tracheal sound; Computer Science - Sound; Computer Science - Sound; Computer Science - Learning; eess.AS
Description
To be submitted, 37 pages, 6 figures, 5 tables, 1 summplementary
table
table
Type
journal article