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Respiratory sound denoising using sparsity-assisted signal smoothing algorithm

Haider Nishi Shahnaj, Behera Ajoy K.

Biocybernetics and Biomedical Engineering

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2022

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Vol. 42, no. 2

481--493

EN

Noises are the unavoidable entities which stands as a big barrier in the field of computerized lung sound (LS) based disease diagnosis, as it impairs the quality of LS and therefore greatly misleads the clinical interpretations done based on that. It has numerous sources, of which a few of them could be the noises due to body sounds, environmental noises, power line noises and recording artifacts, which easily contaminates the LS recordings. This paper presents a novel denoising algorithm to eliminate the noises from LS recordings in a more powerful way using Butterworth band-pass filter and sparsity assisted signal smoothing (SASS) algorithm. This study is carried out over LS captured from 80 Chronic Obstructive Pulmonary Disease (COPD), 80 pneumonia and 80 healthy participants in a clinical environment. Each of the recorded LS is denoised using Butterworth band-pass filter and sparse-assisted signal smoothing algorithm. The denoising performance of the proposed algorithm is evaluated on the basis of denoising performance parameters. As per the evaluation of the denoising performance parameters, it is observed that the proposed denoising method suppressed the LS noises with the signal to noise ratio (SNR) of 66.8 dB and with the peak signal to noise ratio (PSNR) of 78.5 dB. The proposed endeavour can be recommended for clinical use for producing noise free LSs to bring effective interpretations. The future endeavours involve the suppression of the heart sound noises from the LS recordings.

2

Design and development of an e-nose system for the diagnosis of pulmonary diseases

Binson V. A., Subramoniam M.

Acta of Bioengineering and Biomechanics

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2021

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Vol. 23, no. 1

35--44

EN

Purpose: The aim of this paper was to discuss the design and development of an innovative e-nose system which can detect respiratory ailments by detecting the Volatile Organic Compounds (VOCs) in the expelled breath. In addition to nitrogen, oxygen, and carbon dioxide, the expelled breath contains several VOCs, some of which are indicative of lung-related conditions and can differentiate healthy controls from people affected with pulmonary diseases. Methods: This work detailed the sensor selection process, the assembly of the sensors into a sensor array, the design and implementation of the circuit, sampling methods, and an algorithm for data analysis. The clinical feasibility of the system was checked in 27 lung cancer patients, 22 chronic obstructive pulmonary disease (COPD) patients, and 39 healthy controls including smokers and non-smokers. Results: The classification model developed using the support vector machine (SVM) was able to provide accuracy, sensitivity, and specificity of 88.79, 89.58 and 88.23%, respectively for lung cancer, and 78.70, 72.50 and 82.35%, respectively for COPD. Conclusions: The sensor array system developed with TGS gas sensors was non-invasive, low cost, and gave a rapid response. It has been demonstrated that the VOC profiles of patients with pulmonary diseases and healthy controls are different, hence, the e-nose system can be used as a potential diagnostic device for patients with lung diseases.

3

Assessment of Diesel Particulate Matter Exposure of Underground Miners in Indonesia

Susanto A., Purwanto P., Sunoko H. R., Setiani O.

Journal of Ecological Engineering

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2018

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Vol. 19, nr 4

34--42

EN

In Indonesia, there are underground mines for mineral metal such copper (Cu) and gold (Au), built by tunneling towards the mineral location. The purpose of this study was to determine the mapping a concentration of diesel particulate matter (DPM) and assess the impact on health by severity measurement of airflow obstruction of the miners experiencing chronic obstructive pulmonary disease (COPD). The data of DPM were measured with NIOSH method no. 5040 and applied a geostatistical method in mapping concentration at the area of underground mining. A spirometric measurement was conducted to diagnose COPD that is done to the 314 miners. The results showed that the concentrations exceeding the permissible exposure limit (PEL) and spirometric measurement were found for 26 miners (8.3%) who experience COPD (post bronchodilator <0.70). The severity measurement of airflow obstruction of the miners experiencing COPD, severity of airflow limitation for moderate (GOLD 2) was obtained for 14 miners (54%); severe (GOLD 3) for 10 miners (38%) and very severe (GOLD 4) for 2 miners (8%). It can be concluded that the amount of DPM exposure against the severity of airflow limitation with COPD by 0.03, in which the other factors also affect the severity.