Benchmarking of eight recurrent neural network variants for breath phase and adventitious sound detection on a selfdeveloped open-access lung sound database-HF_Lung_V1
Journal
PLoS ONE
Journal Volume
16
Journal Issue
7
Date Issued
2021
Author(s)
Hsu F.-S
Abstract
A reliable, remote, and continuous real-time respiratory sound monitor with automated respiratory sound analysis ability is urgently required in many clinical scenarios-such as in monitoring disease progression of coronavirus disease 2019-to replace conventional auscultation with a handheld stethoscope. However, a robust computerized respiratory sound analysis algorithm for breath phase detection and adventitious sound detection at the recording level has not yet been validated in practical applications. In this study, we developed a lung sound database (HF_Lung_V1) comprising 9,765 audio files of lung sounds (duration of 15 s each), 34,095 inhalation labels, 18,349 exhalation labels, 13,883 continuous adventitious sound (CAS) labels (comprising 8,457 wheeze labels, 686 stridor labels, and 4,740 rhonchus labels), and 15,606 discontinuous adventitious sound labels (all crackles). We conducted benchmark tests using long short-term memory (LSTM), gated recurrent unit (GRU), bidirectional LSTM (BiLSTM), bidirectional GRU (BiGRU), convolutional neural network (CNN)-LSTM, CNN-GRU, CNN-BiLSTM, and CNN-BiGRU models for breath phase detection and adventitious sound detection. We also conducted a performance comparison between the LSTM-based and GRU-based models, between unidirectional and bidirectional models, and between models with and without a CNN. The results revealed that these models exhibited adequate performance in lung sound analysis. The GRU-based models outperformed, in terms of F1 scores and areas under the receiver operating characteristic curves, the LSTM-based models in most of the defined tasks. Furthermore, all bidirectional models outperformed their unidirectional counterparts. Finally, the addition of a CNN improved the accuracy of lung sound analysis, especially in the CAS detection tasks. ? 2021 Hsu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Subjects
abnormal respiratory sound
adult
adventitious sound detection
aged
Article
audio file
benchmarking
bidirectional gated recurrent unit model
bidirectional long short term memory model
breath phase detection
clinical article
continuous adventitious sound label
controlled study
convolutional neural network bidirectional gated recurrent unit model
convolutional neural network bidirectional long short term memory model
convolutional neural network gated recurrent unit model
convolutional neural network long short term memory model
crackle
data base
data processing
diagnostic accuracy
diagnostic test accuracy study
discontinuous adventitious sound label
exhalation label
female
gated recurrent unit model
human
inhalation label
intermethod comparison
long short term memory model
lung sound database
male
model
predictive value
receiver operating characteristic
recurrent neural network
respiratory tract parameters
rhonchus
sensitivity and specificity
sound analysis
sound detection
stridor
wheezing
breathing
diagnosis
disease exacerbation
factual database
lung
middle aged
pathophysiology
very elderly
Adult
Aged
Aged, 80 and over
Benchmarking
COVID-19
Databases, Factual
Disease Progression
Female
Humans
Lung
Male
Middle Aged
Neural Networks, Computer
Respiration
Respiratory Sounds
SDGs
Type
journal article