Pulmonary crackle characterization: approaches in the use of discrete wavelet transform regarding border effect, mother-wavelet selection, and subband reduction

Abstract

Introduction: Crackles are discontinuous, non-stationary respiratory sounds and can be characterized by their duration and frequency. In the literature, many techniques of filtering, feature extraction, and classification were presented. Although the discrete wavelet transform (DWT) is a well-known tool in this area, issues like signal border extension, mother-wavelet selection, and its subbands were not properly discussed. Methods: In this work, 30 different mother-wavelets 8 subbands were assessed, and 9 border extension modes were evaluated. The evaluations were done based on the energy representation of the crackle considering the mother-wavelet and the border extension, allowing a reduction of not representative subbands. Results: Tests revealed that the border extension mode considered during the DWT affects crackle characterization, whereas SP1 (Smooth-Padding of order 1) and ASYMW (Antisymmetric-Padding (whole-point)) modes shall not be used. After DWT, only 3 subbands (D3, D4, and D5) were needed to characterize crackles. Finally, from the group of mother-wavelets tested, Daubechies 7 and Symlet 7 were found to be the most adequate for crackle characterization. Discussion: DWT can be used to characterize crackles when proper border extension mode, mother-wavelet, and subbands are taken into account.

Evaluation of Leaf N Concentration in Winter Wheat Based on Discrete Wavelet Transform Analysis

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“…Low-frequency AC is an expression of global behavior of the signal, which corresponds to the main and large trend in a signal. The AC and DC together reflect the time-frequency properties of the canopy spectral signal at different scales [23][24][25]. It is considered as a productive tool for hyperspectral feature extraction [25], and has been successfully used in quantifying pigment concentrations [28], retrieving soil moisture [29], estimating crop residue mass [26] and leaf area index (LAI) mapping [30].…”

Fractality evaluation for pulmonary crackle sound using the Degree of Self-Similarity

“…The reported results showed that TE with the order of 2, 3, and 4 could produce accuracy up to 95.35%. Another study as reported took only three sub-bands (D3, D4, and D5) to analyze the crackle sound using a wavelet analysis [5]. The suggested mother wavelets were Daubechies 7 and Symlet 7.…”

Influences of the signal border extension in the discrete wavelet transform in EEG spike detection

“…Quandt et al (2015) reported border distortions for respiratory signals. However, for EEG signal, no report on distortions produced by border extension was found.…”