Lung Sound Classification Using Co-Tuning and Stochastic Normalization

Nguyen, Tri Khoa; Pernkopf, Franz

doi:10.1109/tbme.2022.3156293

Cited by 67 publications

(31 citation statements)

References 45 publications

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“…However, overfitting may occur as the layer becomes deeper, which increases the complexity of the model and reduces performance 30 . Based on CNN, several hybrid models have been proposed to compensate for such problems and achieve optimal performance 15 , 28 , 31 . As for the most recent research, a model with performance higher than that of the existing breathing sound classification models by adding artificial noise addition technique to the general CNN structure has been proposed 28 .…”

Section: Discussionmentioning

confidence: 99%

“…As for the most recent research, a model with performance higher than that of the existing breathing sound classification models by adding artificial noise addition technique to the general CNN structure has been proposed 28 . Moreover, a study proposed a model that achieved good performance using the combination of the co-tuning and stochastic normalization techniques of the CNN-based pre-trained ResNet as backbone 15 .…”

Section: Discussionmentioning

confidence: 99%

“…Additionally, there are few studies with pediatric patients, either too few subjects or too few specific types of abnormal sounds 3 , 12 . This limitation in the amount and quality of available data can estimate the performance and generalization of lung sound classification systems 15 .…”

Section: Introductionmentioning

confidence: 99%

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An accurate deep learning model for wheezing in children using real world data

Kim

Mun

et al. 2022

Sci Rep

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Auscultation is an important diagnostic method for lung diseases. However, it is a subjective modality and requires a high degree of expertise. To overcome this constraint, artificial intelligence models are being developed. However, these models require performance improvements and do not reflect the actual clinical situation. We aimed to develop an improved deep-learning model learning to detect wheezing in children, based on data from real clinical practice. In this prospective study, pediatric pulmonologists recorded and verified respiratory sounds in 76 pediatric patients who visited a university hospital in South Korea. In addition, structured data, such as sex, age, and auscultation location, were collected. Using our dataset, we implemented an optimal model by transforming it based on the convolutional neural network model. Finally, we proposed a model using a 34-layer residual network with the convolutional block attention module for audio data and multilayer perceptron layers for tabular data. The proposed model had an accuracy of 91.2%, area under the curve of 89.1%, precision of 94.4%, recall of 81%, and F1-score of 87.2%. The deep-learning model proposed had a high accuracy for detecting wheeze sounds. This high-performance model will be helpful for the accurate diagnosis of respiratory diseases in actual clinical practice.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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An accurate deep learning model for wheezing in children using real world data

Kim

Mun

et al. 2022

Sci Rep

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“…Nguyen and Pernkopf ( 2022 ) proposed a methodology for lung sound classification by employing co-tuning and stochastic normalization to enhance the classification results. They split sound record into 8 s segments then calculating the corrected and normalized spectrogram.…”

Section: Literature Reviewmentioning

confidence: 99%

Deep learning models for detecting respiratory pathologies from raw lung auscultation sounds

2022

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“…The electronic stethoscope can simultaneously collect lung sounds and stethoscope, which simplifies the collection process of lung sounds. Therefore, some studies on intelligent lung sound recognition have used an electronic stethoscope, such as the 3M Littmann 3200, to collect data ( Grzywalski et al, 2019 ; Kevat et al, 2020 ; Hsu et al, 2021 ; Nguyen and Pernkopf, 2022 ). However, the recorded lung sounds still need to be heard by annotators and labeled manually before they can be used in the training algorithm.…”

Section: Introductionmentioning

confidence: 99%

Regularity and mechanism of fake crackle noise in an electronic stethoscope

Li²,

Jian³

et al. 2022

Front. Physiol.

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Background: Electronic stethoscopes are widely used for cardiopulmonary auscultation; their audio recordings are used for the intelligent recognition of cardiopulmonary sounds. However, they generate noise similar to a crackle during use, significantly interfering with clinical diagnosis. This paper will discuss the causes, characteristics, and occurrence rules of the fake crackle and establish a reference for improving the reliability of the electronic stethoscope in lung auscultation.Methods: A total of 56 participants with healthy lungs (no underlying pulmonary disease, no recent respiratory symptoms, and no adventitious lung sound, as confirmed by an acoustic stethoscope) were enrolled in this study. A 30-s audio recording was recorded from each of the nine locations of the larynx and lungs of each participant with a 3M Littmann 3200 electronic stethoscope, and the audio was output in diaphragm mode and auscultated by the clinician. The doctor identified the fake crackles and analyzed their frequency spectrum. High-pass and low-pass filters were used to detect the frequency distribution of the fake crackles. Finally, the fake crackle was artificially regenerated to explore its causes.Results: A total of 500 audio recordings were included in the study, with 61 fake crackle audio recordings. Fake crackles were found predominantly in the lower lung. There were significant differences between lower lung and larynx (p < 0.001), lower lung and upper lung (p = 0.005), lower lung and middle lung (p = 0.005), and lower lung and infrascapular region (p = 0.027). Furthermore, more than 90% of fake crackles appeared in the inspiratory phase, similar to fine crackles, significantly interfering with clinical diagnosis. The spectral analysis revealed that the frequency range of fake crackles was approximately 250–1950 Hz. The fake crackle was generated when the diaphragm of the electronic stethoscope left the skin slightly but not completely.Conclusion: Fake crackles are most likely to be heard when using an electronic stethoscope to auscultate bilateral lower lungs, and the frequency of a fake crackle is close to that of a crackle, likely affecting the clinician’s diagnosis.

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Lung Sound Classification Using Co-Tuning and Stochastic Normalization

Cited by 67 publications

References 45 publications

An accurate deep learning model for wheezing in children using real world data

An accurate deep learning model for wheezing in children using real world data

Deep learning models for detecting respiratory pathologies from raw lung auscultation sounds

Regularity and mechanism of fake crackle noise in an electronic stethoscope

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