Lung Sound Recognition Method Based on Wavelet Feature Enhancement and Time-Frequency Synchronous Modeling

Shi, Lukui; Zhang, Yixuan; Zhang, Jingye

doi:10.1109/jbhi.2022.3210996

Cited by 7 publications

(1 citation statement)

References 41 publications

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“…Their proposed method was evaluated on ICBHI 2017 and outperformed other lightweight models. Shi et al [ 141 ] explored the temporal-frequency information of different scales with the dual wavelet analysis module, and used the attention module to extract the salient difference information for respiratory chronic disease recognition. Multi-types specific RDR.…”

Section: Deep Learning In Lung Sound Analysismentioning

confidence: 99%

Deep learning-based lung sound analysis for intelligent stethoscope

Huang,

Qiao

et al. 2023

Military Med Res

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Auscultation is crucial for the diagnosis of respiratory system diseases. However, traditional stethoscopes have inherent limitations, such as inter-listener variability and subjectivity, and they cannot record respiratory sounds for offline/retrospective diagnosis or remote prescriptions in telemedicine. The emergence of digital stethoscopes has overcome these limitations by allowing physicians to store and share respiratory sounds for consultation and education. On this basis, machine learning, particularly deep learning, enables the fully-automatic analysis of lung sounds that may pave the way for intelligent stethoscopes. This review thus aims to provide a comprehensive overview of deep learning algorithms used for lung sound analysis to emphasize the significance of artificial intelligence (AI) in this field. We focus on each component of deep learning-based lung sound analysis systems, including the task categories, public datasets, denoising methods, and, most importantly, existing deep learning methods, i.e., the state-of-the-art approaches to convert lung sounds into two-dimensional (2D) spectrograms and use convolutional neural networks for the end-to-end recognition of respiratory diseases or abnormal lung sounds. Additionally, this review highlights current challenges in this field, including the variety of devices, noise sensitivity, and poor interpretability of deep models. To address the poor reproducibility and variety of deep learning in this field, this review also provides a scalable and flexible open-source framework that aims to standardize the algorithmic workflow and provide a solid basis for replication and future extension: https://github.com/contactless-healthcare/Deep-Learning-for-Lung-Sound-Analysis.

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Section: Deep Learning In Lung Sound Analysismentioning

confidence: 99%