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An acoustical respiratory phase segmentation algorithm using genetic approach

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Abstract

This paper proposes a robust and fully automated respiratory phase segmentation method using single channel tracheal breath sounds (TBS) recordings of different types. The estimated number of respiratory segments in a TBS signal is firstly obtained based on noise estimation and nonlinear mapping. Respiratory phase boundaries are then located through the generations of multi-population genetic algorithm by introducing a new evaluation function based on sample entropy (SampEn) and a heterogeneity measure. The performance of the proposed method is analyzed for single channel TBS recordings of various types. An overall respiratory phase segmentation accuracy is found to be 12 ± 5 ms for normal TBS and 21 ± 9 ms for adventitious sounds. The results show the robustness and effectiveness of the proposed segmentation method. The proposed method has been a successful attempt to solve the clinical application challenge faced by the existing phase segmentation methods in terms of respiratory dysfunctions.

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Acknowledgments

The authors gratefully acknowledge the contribution of National University Hospital, especially Dr. Irene Melinda Louis, for their support in data collection and identification. The authors are also grateful to the Reviewers and the Editor for their valuable comments and suggestions that help to improve the paper significantly.

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Authors and Affiliations

  1. School of Electrical and Electronic Engineering, Nanyang Technological University, Nanyang Avenue 50, Singapore, 639798, Singapore

    F. Jin & F. Sattar

  2. Paediatrics Department, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore

    D. Y. T. Goh

Authors
  1. F. Jin
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  2. F. Sattar
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  3. D. Y. T. Goh
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Corresponding author

Correspondence to F. Jin.

Appendix 1

Appendix 1

SampEn(m, r, N) can be calculated as below:

For an input signal U of length N, {U(p):1 ≤ p ≤ N} forms the N−m + 1 vectors x m (q) for {q|1 ≤ q ≤ N−m + 1}, where x m (q) = {U(q + b):0 ≤ b ≤ m−1} is the vector of m data points from U(q) to U(q + m−1). In this context, only the first N−m vectors of length m are considered to ensure that, x m (q) and x m+1(q) are defined for 1 ≤ q ≤ N−m. Let B m(r) be the probability that two sequences will match for m points and A m(r) is the probability that two sequences will match for m + 1 points. B m q (r) is defined as (N−m−1)−1 times the numbers of vectors x m (p) within r of x m (q), where 1 ≤ p ≤ N−m, and p ≠ q to exclude self-matches. Then B m(r) is defined as

$$ B^{m}(r)=(N-m)^{-1}\sum\limits_{q=1}^{N-1}B^{m}_{q}(r) $$
(15)

Similarly, A m q (r) is defined as (N−m−1)−1 times the numbers of vectors x m+1(p) within r of x m+1(q), where 1 ≤ p ≤ N−m and p ≠ q. Then set A m(r) as

$$ A^{m}(r)=(N-m)^{-1}\sum\limits_{q=1}^{N-1}A^{m}_{q}(r) $$
(16)

Finally, sample entropy (SampEn) is calculated by

$$ SampEn(m,r,N)=-\ln{\frac{A^{m}(r)} {B^{m}(r)}}. $$
(17)

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Jin, F., Sattar, F. & Goh, D.Y.T. An acoustical respiratory phase segmentation algorithm using genetic approach. Med Biol Eng Comput 47, 941–953 (2009). https://doi.org/10.1007/s11517-009-0518-0

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  • DOI: https://doi.org/10.1007/s11517-009-0518-0

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