Parallel source separation system for heart and lung sounds

Muñoz-Montoro, Antonio J.; Suarez-Dou, D.; Cortina, Raquel; Canadas-Quesada, F.; Combarro, Elías F.

doi:10.1007/s11227-020-03616-0

Cited by 6 publications

(2 citation statements)

References 24 publications

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“…While DL techniques offer ideal capabilities for feature extraction during HLS separation, the diverse nature of HLS signals can undermine the reliability of neural networks. Lin et al [ 142 ] NMF-based constant Q transform for dimensionality reduction Shah et al [ 143 ] An unsupervised BSS and enhanced NMF with shared factors and method that does not require any training data Canadas et al [ 144 ] A spectrotemporal clustering by NMF Montoro et al [ 145 ] A parallel source partitioning system derived from NMF Sathesh et al [ 146 ] HS real-time signals from LS signals with ANN Tsai et al [ 26 ] A periodical deep autoencoder encoded method …”

Section: Discussion Of the System Elementsmentioning

confidence: 99%

Lung disease recognition methods using audio-based analysis with machine learning

Sabry,

I. Dallal Bashi,

Nik Ali

et al. 2024

Heliyon

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Section: Discussion Of the System Elementsmentioning

confidence: 99%

Lung disease recognition methods using audio-based analysis with machine learning

Sabry,

I. Dallal Bashi,

Nik Ali

et al. 2024

Heliyon

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“…As the recording environments can be noisy with environmental noise and speech, denoising is an essential pre-processing procedure to improve the audio quality for better performance of segmentation and classification. Other pre-processing procedures such as separation (from lung sounds) [18]- [20] are not introduced here, as they were mostly dealing with signal processing methods.…”

Section: B Challenges In Heart Sound Analysismentioning

confidence: 99%

A Comprehensive Survey on Heart Sound Analysis in the Deep Learning Era

Zhao¹,

Chen²,

Nguyên³

et al. 2023

Preprint

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Heart sound auscultation has been demonstrated to be beneficial in clinical usage for early screening of cardiovascular diseases. Due to the high requirement of well-trained professionals for auscultation, automatic auscultation benefiting from signal processing and machine learning can help auxiliary diagnosis and reduce the burdens of training professional clinicians. Nevertheless, classic machine learning is limited to performance improvement in the era of big data. Deep learning has achieved better performance than classic machine learning in many research fields, as it employs more complex model architectures with stronger capability of extracting effective representations. Deep learning has been successfully applied to heart sound analysis in the past years. As most review works about heart sound analysis were given before 2017, the present survey is the first to work on a comprehensive overview to summarise papers on heart sound analysis with deep learning in the past six years 2017-2022. We introduce both classic machine learning and deep learning for comparison, and further offer insights about the advances and future research directions in deep learning for heart sound analysis.

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Heart-lung sound separation by nonnegative matrix factorization and deep learning

Wang

Qin

et al. 2023

Biomedical Signal Processing and Control

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Parallel source separation system for heart and lung sounds

Cited by 6 publications

References 24 publications

Lung disease recognition methods using audio-based analysis with machine learning

Lung disease recognition methods using audio-based analysis with machine learning

A Comprehensive Survey on Heart Sound Analysis in the Deep Learning Era

Heart-lung sound separation by nonnegative matrix factorization and deep learning

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