Using nonlocal means to separate cardiac and respiration sounds

Rudnitskii, A. G.

doi:10.1134/s1063771014050121

Cited by 11 publications

(5 citation statements)

References 17 publications

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“…This criterion exploits the frequency bands where both sounds predominate. Heart sounds predominate in the frequency bands between 30-120 Hz (S 1 ) and 70-150 Hz (S 2 ), 20 while lung sounds are concentrated in the 20-100 Hz frequency band. 14 Therefore, heart sounds tend to be concentrated in a slightly higher frequency band than lung sounds.…”

Section: Spectral Distributionmentioning

confidence: 99%

“…4,7,18,19 Their frequency spectrum is typically defined between 10-300 Hz for S 1 and 50-320 Hz for S 2 , 13 mainly concentrated in the 30-120 Hz and 70-150 Hz ranges respectively. 20 If the patient has heart murmurs, the frequency spectrum could extend to 500 Hz or even higher. 6,17 These murmurs are related to the third and fourth heart sounds (S 3 and S 4 ), which are less recurrent.…”

Section: Introductionmentioning

confidence: 99%

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Source separation for single channel thoracic cardio-respiratory sounds applying Non-negative Matrix Factorization (NMF) using a focused strategy on heart sound positions

Arce¹,

Cuadra

2023

18th International Symposium on Medical Information Processing and Analysis

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Auscultation using stethoscopes allows the diagnosis of respiratory and cardiac diseases. However, these sounds interfere with each other both in time and frequency. In the case of recording heart sounds, it is possible to ask the patient to stop their breathing to perform auscultation and obtain a pure heart sound. But, in the case of lung sounds it is impossible to do the same. In this paper, a source separation method based on Non-negative Matrix Factorization (NMF) is used to decompose a signal into different c omponents. T he m ethod proposed uses information from the estimated lung sound to reinsert the segments of interest into the original signal. The objective of this approximation is not to distort the segments of pure respiratory sound (free of heart sound). This method is compared to a base case of NMF decomposition on the raw signal. Three criteria for classifying the components based on the literature are also proposed, which will allow to indicate which component corresponds to each sound. The results were evaluated using temporal and spectral correlations, mean square error (MSE) and signal to distortion ratio (SDR) between the original respiratory signal and the respiratory signal estimated through the algorithm. It is shown that the best approximation is the NMF decomposition on the entire signal & replacing segments under different p arameter variations.

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Section: Spectral Distributionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Source separation for single channel thoracic cardio-respiratory sounds applying Non-negative Matrix Factorization (NMF) using a focused strategy on heart sound positions

Arce¹,

Cuadra

2023

18th International Symposium on Medical Information Processing and Analysis

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show abstract

“…Щоб реалізувати переваги комп'ютерної аускультації, загальний сигнал слід розділити на його природні складові: дихальні шуми, звуки серця і стаціонарні фонові шуми. На сьогоднішній день існує ряд підходів до розв'язання цього питання [1][2][3]. У даній роботі для розділення аускультативного сигналу на його природні складові використовується комбінований підхід, заснований на методах математичної морфології (ММ) [4] і байєсівській оцінці шумової перешкоди.…”

Section: введенняunclassified

Single-Channel Processing of Auscultatory Signals Using Methods of Mathematical Morphology

Rudnitskii¹,

Rudnytska²,

Tkachenko³

2021

JNAM

Self Cite

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The paper considers a new method of separating respiratory sounds from heart sounds in a general signal registered on the surface of the human body. The proposed approach is based on a combination of Bayesian noise suppression techniques and methods of mathematical morphology. The proposed method was tested on real auscultatory signals. Evaluation of the efficiency of the algorithm using auditory, visual and numerical analysis shows that the developed approach is a promising alternative to existing techniques for separating auscultatory signals into its natural components.

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“…This prevents the proper diagnosis of patients. Consequently, several methods have been developed in the last decade to separate or independently process heart and lung sounds from auscultation signals [6,[22][23][24][25][26][27][28][29][30][31][32].…”

Section: Introductionmentioning

confidence: 99%

A system for biomedical audio signal processing based on high performance computing techniques

Muñoz-Montoro¹,

Revuelta-Sanz²,

Villalón-Fernández³

et al. 2022

ICA

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In this paper, a noninvasive portable prototype is presented for biomedical audio signal processing. The proposed prototype is suitable for monitoring the health of patients. The proposed hardware setup consists of a cost-effective microphone, multipurpose microcontroller and computing node that could be a mobile phone or general-purpose computer. Using parallel and high-performance techniques, this setup allows one to register and wirelessly multicast the recorded biomedical signals to computing nodes in real time. The developed prototype was used as a case study to estimate the heart rate (HR) from the captured biomedical audio signal. In this regard, the developed algorithm for estimating HR comprises three stages: preprocessing, separation, and HR estimation. In the first stage, the signal captured by the microphone is adapted for processing. Subsequently, a separation stage was proposed to alleviate the acoustic interference between the lungs and heart. The separation is performed by combining a non-negative matrix factorization algorithm, clustering approach, and soft-filter strategy. Finally, HR estimation was obtained using a novel and efficient method based on the autocorrelation function. The developed prototype could be used not only for the estimation of the HR, but also for the retrieval of other biomedical information related to the recording of cardiac or respiratory audio signals. The proposed method was evaluated using well-known datasets and compared with state-of-the-art algorithms for source-separation. The results showed that it is possible to obtain an accurate separation and reliable real-time estimation in terms of source separation metrics and relative error in the tested scenarios by combining multi-core architectures with parallel and high-performance techniques. Finally, the proposed prototype was validated in a real-world scenario.

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Using nonlocal means to separate cardiac and respiration sounds

Cited by 11 publications

References 17 publications

Source separation for single channel thoracic cardio-respiratory sounds applying Non-negative Matrix Factorization (NMF) using a focused strategy on heart sound positions

Source separation for single channel thoracic cardio-respiratory sounds applying Non-negative Matrix Factorization (NMF) using a focused strategy on heart sound positions

Single-Channel Processing of Auscultatory Signals Using Methods of Mathematical Morphology

A system for biomedical audio signal processing based on high performance computing techniques

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