Restoration of lung sound signals using a hybrid wavelet-based approach

Lee, C. S., Li, M., Lou, Y. and Dahiya, R. (2022) Restoration of lung sound signals using a hybrid wavelet-based approach. IEEE Sensors Journal, 22(20), pp. 19700-19712. (doi: 10.1109/JSEN.2022.3203391)

Abstract

A unique and ideal integration of wavelet-based total variation (WATV) and empirical Wiener denoising method is proposed in this article to significantly enhance the signal-to-noise ratio (SNR) while preserving the characteristics of a lung sound signal. While individual wavelet-based denoising filters based on a single basis function have been employed in the past, the outcome has been unsatisfactory because only significant (signal) wavelet coefficients are considered for denoising analysis. The new wavelet-based empirical Wiener (WATV-Wiener) hybrid technique, proposed here, takes into account both significant and insignificant (noise) wavelet coefficients of the noisy signal. An intensive analysis of selecting and fine-tuning the WATV-Wiener filter parameters is presented here through the simulation studies. The WATV-Wiener filter applied here onto different 1-D lung sound signals of different noise levels has led to an optimal root mean square error (RMSE) compared with seven other state-of-the-art filters reported in the literature. The optimal parameters achieved through our simulation studies led to a 3–20-dB improvement in SNR, and the average SNR was improved by 4–30 dB in our experiment. We also observed that the WATV-Wiener filter is less sensitive to the variation of SNR values of the input signal. Furthermore, the WATV-Wiener filter obtains similar SNR performance between continuous piecewise signal (wheeze) and noncontinuous piecewise signal (crackle) in both simulation and experimental studies.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Dahiya, Professor Ravinder and Lee, Chang Sheng
• Authors: Lee, C. S., Li, M., Lou, Y., and Dahiya, R.
• College/School: College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
• Journal Name: IEEE Sensors Journal
• Publisher: IEEE
• ISSN: 1530-437X
• ISSN (Online): 1558-1748
• Published Online: 08 September 2022
• Copyright Holders: Copyright © 2022 IEEE
• First Published: First published in IEEE Sensors Journal 22(20):19700-19712
• Publisher Policy: Reproduced in accordance with the publisher copyright policy