Gabrieli Giulio, Bornstein Marc H, Manian Nanmathi, Esposito Gianluca
Psychology Program, School of Social Sciences, Nanyang Technological University, Singapore 639818, Singapore.
Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, MD 20892, USA.
Behav Sci (Basel). 2020 Feb 6;10(2):55. doi: 10.3390/bs10020055.
Postpartum Depression (PPD), a condition that affects up to 15% of mothers in high-income countries, reduces attention to the needs of the child and is among the first causes of infanticide. PPD is usually identified using self-report measures and therefore it is possible that mothers are unwilling to report PPD because of a social desirability bias. Previous studies have highlighted the presence of significant differences in the acoustical properties of the vocalizations of infants of depressed and healthy mothers, suggesting that the mothers' behavior can induce changes in infants' vocalizations. In this study, cry episodes of infants (N = 56, 157.4 days ± 8.5, 62% firstborn) of depressed (N = 29) and non-depressed (N = 27) mothers (mean age = 31.1 years ± 3.9) are analyzed to investigate the possibility that a cloud-based machine learning model can identify PPD in mothers from the acoustical properties of their infants' vocalizations. Acoustic features (fundamental frequency, first four formants, and intensity) are first extracted from recordings of crying infants, then cloud-based artificial intelligence models are employed to identify maternal depression versus non-depression from estimated features. The trained model shows that commonly adopted acoustical features can be successfully used to identify postpartum depressed mothers with high accuracy (89.5%).
产后抑郁症（PPD）在高收入国家影响着多达15%的母亲，它会降低对孩子需求的关注度，并且是杀婴的首要原因之一。PPD通常通过自我报告测量来识别，因此母亲可能由于社会期望偏差而不愿报告PPD。先前的研究强调了抑郁母亲和健康母亲的婴儿发声的声学特性存在显著差异，这表明母亲的行为会引起婴儿发声的变化。在本研究中，对抑郁母亲（N = 29）和非抑郁母亲（N = 27）（平均年龄 = 31.1岁±3.9）的婴儿（N = 56，157.4天±8.5，62%为头胎）的哭声发作进行了分析，以研究基于云的机器学习模型能否根据婴儿发声的声学特性识别母亲是否患有PPD。首先从哭闹婴儿的录音中提取声学特征（基频、前四个共振峰和强度），然后使用基于云的人工智能模型根据估计特征识别母亲是否抑郁。训练后的模型表明，常用的声学特征可以成功地用于高精度（89.5%）识别产后抑郁的母亲。
