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Characterization of infant healthy and pathological cry signals in cepstrum domain based on approximate entropy and correlation dimension

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  • Lahmiri, Salim
  • Tadj, Chakib
  • Gargour, Christian
  • Bekiros, Stelios

Abstract

The analysis of infant cry signals is becoming an attractive field of research in biomedical physics and engineering for better understanding of the pathologies and appropriate medial diagnosis. The main purpose of the current study is to characterize infant normal and pathological cry signals by studying their respective oscillations by means of approximate entropy and correlation dimension estimated from their respective cepstrums. We analyzed two different sets. The first one is composed of 2638 expiration cry signals and the second set is composed of 1860 inspiration cry signals, both sets equally weighted. After estimating approximate entropy and correlation dimensions from cepstrums, three standard statistical tests are applied to them including the Student t-test, F-test, and two-sample Kolmogorov-Smirnov test. All statistical tests are performed at 5% statistical significance level. The empirical results follow. First, approximate entropy and correlation dimension measures exhibit different statistical characteristics across healthy and unhealthy infant cries from both expiration and inspiration sets. Second, the level of approximate entropy in cepstrums of healthy infant cries is statistically higher than that in cepstrums of unhealthy infant cries. Third, the level of correlation dimension in cepstrums of healthy infant cries is statistically higher than that in cepstrums of unhealthy infant cries. In other words, cepstrums of healthy infant cries show lower randomness and disorder compared to cepstrums of unhealthy infant cries. It is concluded that cepstrum-based approximate entropy and correlation dimension discriminate healthy from pathological infant cry signals and can be employed as effective biomarkers for biomedical diagnosis of cry records in clinical milieu.

Suggested Citation

  • Lahmiri, Salim & Tadj, Chakib & Gargour, Christian & Bekiros, Stelios, 2021. "Characterization of infant healthy and pathological cry signals in cepstrum domain based on approximate entropy and correlation dimension," Chaos, Solitons & Fractals, Elsevier, vol. 143(C).
    • Handle: RePEc:eee:chsofr:v:143:y:2021:i:c:s0960077920310304
    DOI: 10.1016/j.chaos.2020.110639
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    References listed on IDEAS

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    1. Tang, Pingzhou & Chen, Di & Hou, Yushuo, 2016. "Entropy method combined with extreme learning machine method for the short-term photovoltaic power generation forecasting," Chaos, Solitons & Fractals, Elsevier, vol. 89(C), pages 243-248.
    2. Nie, Chun-Xiao, 2019. "Applying correlation dimension to the analysis of the evolution of network structure," Chaos, Solitons & Fractals, Elsevier, vol. 123(C), pages 294-303.
    3. Pham, Tuan D. & Yan, Hong, 2018. "A regularity statistic for images," Chaos, Solitons & Fractals, Elsevier, vol. 106(C), pages 227-232.
    4. Pham, Tuan D., 2012. "Regularity dimension of sequences and its application to phylogenetic tree reconstruction," Chaos, Solitons & Fractals, Elsevier, vol. 45(6), pages 879-887.
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    Cited by:

    1. Zhou, Shuang & Wang, Xingyuan & Zhou, Wenjie & Zhang, Chuan, 2022. "Recognition of the scale-free interval for calculating the correlation dimension using machine learning from chaotic time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 588(C).
    2. Lahmiri, Salim & Tadj, Chakib & Gargour, Christian & Bekiros, Stelios, 2023. "Optimal tuning of support vector machines and k-NN algorithm by using Bayesian optimization for newborn cry signal diagnosis based on audio signal processing features," Chaos, Solitons & Fractals, Elsevier, vol. 167(C).
    3. Lahmiri, Salim & Tadj, Chakib & Gargour, Christian & Bekiros, Stelios, 2022. "Deep learning systems for automatic diagnosis of infant cry signals," Chaos, Solitons & Fractals, Elsevier, vol. 154(C).

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