Assessing mothers' postpartum depression from their infants' cry vocalizations
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...
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sg-ntu-dr.10356-1432422020-08-24T07:48:53Z Assessing mothers' postpartum depression from their infants' cry vocalizations Gabrieli, Giulio Bornstein, Marc H. Manian, Nanmathi Esposito, Gianluca School of Social Sciences Lee Kong Chian School of Medicine (LKCMedicine) Social sciences::Psychology Infant Cry Postpartum Depression 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%). Nanyang Technological University Published version This research was supported by Nanyang Technological University (Singapore) under the NAP-SUG grant, the Intramural Research Program of the NIH/NICHD, USA, and an International Research Fellowship at the Institute for Fiscal Studies (IFS), London, UK, funded by the European Research Council (ERC) under the Horizon 2020 research and innovation programme (grant agreement No 695300-HKADeC-ERC-2015-AdG). 2020-08-14T06:28:22Z 2020-08-14T06:28:22Z 2020 Journal Article 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 2076-328X https://hdl.handle.net/10356/143242 10.3390/bs10020055 32041121 2-s2.0-85081668679 2 10 en NAP-SUG Behavioral Sciences © 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/). application/pdf |
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Social sciences::Psychology Infant Cry Postpartum Depression Gabrieli, Giulio Bornstein, Marc H. Manian, Nanmathi Esposito, Gianluca Assessing mothers' postpartum depression from their infants' cry vocalizations |
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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%). |
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School of Social Sciences |
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School of Social Sciences Gabrieli, Giulio Bornstein, Marc H. Manian, Nanmathi Esposito, Gianluca |
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Article |
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Gabrieli, Giulio Bornstein, Marc H. Manian, Nanmathi Esposito, Gianluca |
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Gabrieli, Giulio |
title |
Assessing mothers' postpartum depression from their infants' cry vocalizations |
title_short |
Assessing mothers' postpartum depression from their infants' cry vocalizations |
title_full |
Assessing mothers' postpartum depression from their infants' cry vocalizations |
title_fullStr |
Assessing mothers' postpartum depression from their infants' cry vocalizations |
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Assessing mothers' postpartum depression from their infants' cry vocalizations |
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assessing mothers' postpartum depression from their infants' cry vocalizations |
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2020 |
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https://hdl.handle.net/10356/143242 |
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1681059656210841600 |