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Hybrid method for noise rejection from breath sound using transient artifact reduction algorithm and spectral subtraction

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Published/Copyright: March 21, 2024
Biomedical Engineering / Biomedizinische Technik
From the journal Biomedical Engineering / Biomedizinische Technik Volume 69 Issue 5

Abstract

Objectives

Computerized breath sound based diagnostic methods are one of the emerging technologies gaining popularity in terms of detecting respiratory disorders. However, the breath sound signal used in such automated systems used to be too noisy, which affects the quality of the diagnostic interpretations. To address this problem, the proposed work presents the new hybrid approach to reject the noises from breath sound.

Methods

In this method, 80 chronic obstructive pulmonary disease (COPD), 75 asthmatics and 80 normal breath sounds were recorded from the participants of a hospital. Each of these breath sound data were decontaminated using hybrid method of Butterworth band-pass filter, transient artifact reduction algorithm and spectral subtraction algorithm. The study examined the algorithms noise rejection potential over each category of breath sound by estimating the noise rejection performance metrics, i.e., mean absolute error (MAE), mean square error (MSE), peak signal to noise ratio (PSNR), and signal to noise ratio (SNR).

Results

Using this algorithm, the study obtained a high value of SNR of 70 dB and that of PSNR of 72 dB.

Conclusions

The study could definitely a suitable one to suppress noises and to produce noise free breath sound signal.

Keywords: asthma; COPD; denoise; breath sound; spectral subtraction; transient artifact reduction algorithm
Corresponding author: Nishi Shahnaj Haider, Department of Electronics and Instrumentation Engineering, 154018 Ramaiah Institute of Technology , Bangalore, Karnataka, 560054, India, Phone: +91-6261530231, E-mail:

  1. Research ethics: The proposed study involves the data collection from the human participants. The entire data collection procedure strictly follows the Declaration of Helsinki, as approved by the institutional Ethical Committee.

  2. Informed consent: Written informed consent was collected from the individuals before enrolling them for the data collection.

  3. Author contributions: The author Dr. Nishi Shahnaj Haider has contributed in preparation of the relevant software coding and writing of the manuscript. Author Dr. Ajoy K. Behera has contributed to data collection from the hospital.

  4. Competing interests: The authors have no relevant financial or non-financial interests to disclose.

  5. Research funding: The authors state that the submitted work is not financially supported by any organization.

  6. Data availability: Authors have collected data, however, they do not have the rights to share the data.

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Received: 2023-09-14
Accepted: 2024-03-06
Published Online: 2024-03-21
Published in Print: 2024-10-28

© 2024 Walter de Gruyter GmbH, Berlin/Boston

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https://doi.org/10.1515/bmt-2023-0426
Keywords for this article
asthma; COPD; denoise; breath sound; spectral subtraction; transient artifact reduction algorithm

Articles in the same Issue

  1. Frontmatter
  2. Research Articles
  3. Sparse-view CT reconstruction based on group-based sparse representation using weighted guided image filtering
  4. Comparative evaluation of volumetry estimation from plain and contrast enhanced computed tomography liver images
  5. Vein segmentation and visualization of upper and lower extremities using convolution neural network
  6. STF-Net: sparsification transformer coding guided network for subcortical brain structure segmentation
  7. Machine learning based endothelial cell image analysis of patients undergoing descemet membrane endothelial keratoplasty surgery
  8. AML leukocyte classification method for small samples based on ACGAN
  9. Deep learning classification of EEG-based BCI monitoring of the attempted arm and hand movements
  10. Hybrid method for noise rejection from breath sound using transient artifact reduction algorithm and spectral subtraction
  11. Optimized Schlieren imaging for real-time visualization of high-intensity focused ultrasound waves
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