Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/143242
Title: Assessing mothers' postpartum depression from their infants' cry vocalizations
Authors: Gabrieli, Giulio
Bornstein, Marc H.
Manian, Nanmathi
Esposito, Gianluca
Keywords: Social sciences::Psychology
Issue Date: 2020
Source: Gabrieli, G., Bornstein, M. H., Manian, N., & Esposito, G. (2020). Assessing mothers' postpartum depression from their infants' cry vocalizations. Behavioral Sciences, 10(2), 55-. doi:10.3390/bs10020055
Project: NAP-SUG
Journal: Behavioral Sciences
Abstract: 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%).
URI: https://hdl.handle.net/10356/143242
ISSN: 2076-328X
DOI: 10.3390/bs10020055
Rights: © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SSS Journal Articles

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