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.

婴儿啼哭信号的分析正成为生物医学物理学和工程学中的一个有吸引力的研究领域，目的是更好地了解病理情况和适当的中间诊断。当前研究的主要目的是通过根据其相应的倒频谱估计的近似熵和相关维数研究它们各自的振动，从而表征婴儿的正常和病理性哭泣信号。我们分析了两个不同的集合。第一组由2638个呼气呼号组成，第二组由1860个吸气呼号组成，两个组的权重均相等。在估计倒包的近似熵和相关维数之后，对它们应用了三个标准统计检验，包括Student t检验，F测试和两次抽样的Kolmogorov-Smirnov测试。所有统计检验均按5％的统计显着性水平进行。实证结果如下。首先，从熵集和吸气集来看，健康和不健康婴儿哭声的近似熵和相关维数度量都显示出不同的统计特征。第二，健康婴儿哭声的倒囊的近似熵水平在统计学上高于不健康婴儿哭声的倒囊的熵水平。第三，健康婴儿哭声的倒囊的相关维度水平在统计学上高于不健康婴儿哭声的倒囊的相关维度。换句话说，与不健康的婴儿哭泣的倒囊相比，健康的婴儿哭泣的倒囊显示出较低的随机性和混乱性。