Assessing Mothers’ Postpartum Depression From Their Infants’ Cry Vocalizations
1
Psychology Program, School of Social Sciences, Nanyang Technological University, Singapore 639818, Singapore
2
Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, MD 20892, USA
3
Institute for Fiscal Studies, London WC1E 7AE, UK
4
Westat, Rockville, MD 20850, USA
5
Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore 308232, Singapore
6
Department of Psychology and Cognitive Science, University of Trento, 38068 Trento, Italy
*
Author to whom correspondence should be addressed.
Behav. Sci. 2020, 10(2), 55; https://doi.org/10.3390/bs10020055
Received: 14 January 2020
/
Revised: 31 January 2020
/
Accepted: 2 February 2020
/
Published: 6 February 2020
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%).
View Full-Text
Keywords:
infant cry; postpartum depression; acoustic analysis
▼
Show Figures
Graphical abstract
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
-
Externally hosted supplementary file 1
Doi: 10.21979/N9/IU0UOB
Link: https://doi.org/10.21979/N9/IU0UOB
Description: Related Data for: Assessing mothers’ post-partum depression from their infants’ cry vocalizations
MDPI and ACS Style
Gabrieli, G.; Bornstein, M.H.; Manian, N.; Esposito, G. Assessing Mothers’ Postpartum Depression From Their Infants’ Cry Vocalizations. Behav. Sci. 2020, 10, 55. https://doi.org/10.3390/bs10020055
AMA Style
Gabrieli G, Bornstein MH, Manian N, Esposito G. Assessing Mothers’ Postpartum Depression From Their Infants’ Cry Vocalizations. Behavioral Sciences. 2020; 10(2):55. https://doi.org/10.3390/bs10020055
Chicago/Turabian StyleGabrieli, Giulio, Marc H. Bornstein, Nanmathi Manian, and Gianluca Esposito. 2020. "Assessing Mothers’ Postpartum Depression From Their Infants’ Cry Vocalizations" Behavioral Sciences 10, no. 2: 55. https://doi.org/10.3390/bs10020055
Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.
