Dataset Information

Defining and distinguishing infant behavioral states using acoustic cry analysis: is colic painful?

ABSTRACT:

Background

To characterize acoustic features of an infant's cry and use machine learning to provide an objective measurement of behavioral state in a cry-translator. To apply the cry-translation algorithm to colic hypothesizing that these cries sound painful.

Methods

Assessment of 1000 cries in a mobile app (ChatterBaby^TM). Training a cry-translation algorithm by evaluating >6000 acoustic features to predict whether infant cry was due to a pain (vaccinations, ear-piercings), fussy, or hunger states. Using the algorithm to predict the behavioral state of infants with reported colic.

Results

The cry-translation algorithm was 90.7% accurate for identifying pain cries, and achieved 71.5% accuracy in discriminating cries from fussiness, hunger, or pain. The ChatterBaby cry-translation algorithm overwhelmingly predicted that colic cries were most likely from pain, compared to fussy and hungry states. Colic cries had average pain ratings of 73%, significantly greater than the pain measurements found in fussiness and hunger (p < 0.001, 2-sample t test). Colic cries outranked pain cries by measures of acoustic intensity, including energy, length of voiced periods, and fundamental frequency/pitch, while fussy and hungry cries showed reduced intensity measures compared to pain and colic.

Conclusions

Acoustic features of cries are consistent across a diverse infant population and can be utilized as objective markers of pain, hunger, and fussiness. The ChatterBaby algorithm detected significant acoustic similarities between colic and painful cries, suggesting that they may share a neuronal pathway.

SUBMITTER: Parga JJ

PROVIDER: S-EPMC7033040 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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Publications

Defining and distinguishing infant behavioral states using acoustic cry analysis: is colic painful?

Parga Joanna J JJ Lewin Sharon S Lewis Juanita J Montoya-Williams Diana D Alwan Abeer A Shaul Brianna B Han Carol C Bookheimer Susan Y SY Eyer Sherry S Dapretto Mirella M Zeltzer Lonnie L Dunlap Lauren L Nookala Usha U Sun Daniel D Dang Bianca H BH Anderson Ariana E AE

Pediatric research 20191004 3

<h4>Background</h4>To characterize acoustic features of an infant's cry and use machine learning to provide an objective measurement of behavioral state in a cry-translator. To apply the cry-translation algorithm to colic hypothesizing that these cries sound painful.<h4>Methods</h4>Assessment of 1000 cries in a mobile app (ChatterBaby<sup>TM</sup>). Training a cry-translation algorithm by evaluating >6000 acoustic features to predict whether infant cry was due to a pain (vaccinations, ear-pierci ...[more]

PMID: 31585457

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Project description:ImportanceAcoustic cry characteristics have been associated with severe medical problems in newborns. However, little is known about the utility of neonatal acoustic cry characteristics in the prediction of long-term outcomes of very preterm infants.ObjectivesTo evaluate whether acoustic characteristics of infant cry at neonatal intensive care unit (NICU) discharge are associated with behavioral and developmental outcomes at age 2 years in infants born very preterm.Design, setting, and participantsInfants born less than 30 weeks postmenstrual age (PMA) were enrolled from April 2014 through June 2016 as part of a multicenter (9 US university affiliated NICUs) cohort study and followed to adjusted age 2 years. Reported analyses began on September 2021. Data were analyzed from September 2021 to September 2022.ExposuresThe primary exposure was premature birth (<30 weeks PMA).Main outcomes and measuresCries were recorded during a neurobehavioral examination administered during the week of NICU discharge. Cry episodes were analyzed using a previously published computerized system to characterize cry acoustics. Year-2 outcomes included the Bayley-III Composite scores, Child Behavior Checklist (CBCL) and the Modified Checklist for Autism in Toddlers (M-CHAT R/F), dichotomized using clinically significant cutoffs (<85 on Bayley Language, Cognitive and/or Motor Composite scores, T-score >63 on the CBCL Internalizing, Externalizing and/or Total Problem Scales and total M-CHAT R/F score >2).ResultsAnalyzed infants (363 participants) were primarily male (202 participants [55.65%]) and had a mean [SD] gestational age of 27.08 [1.95] weeks). Cross-validated random forest models revealed that cry acoustics were associated with 2-year outcomes. Tests of diagnostic odds ratios (DOR) revealed that infants who exhibited total problem behavior CBCL scores greater than 63 at age 2 years were 3.3 times more likely (95% CI, 1.44-7.49) to be identified as so by random forest model estimates relative to other infants (scores ≤63); this association was robust to adjustment for family-wise type-I error rates and covariate measures. Similar associations were observed for internalizing (DOR, 2.39; 95% CI, 1.04-5.47) and externalizing (DOR, 2.25; 95% CI, 1.12-4.54) scores on the CBCL, clinically significant language (DOR, 1.71; 95% CI, 1.10-2.67) and cognitive (DOR, 1.70; 95% CI, 1.00-2.88) scores on the Bayley-III, and a positive autism screen on the M-CHAT (DOR, 1.91; 95% CI, 1.05-3.44).Conclusions and relevanceIn this cohort study of preterm infants, findings pointed to the potential use of acoustic cry characteristics in the early identification of risk for long-term developmental and behavioral deficits.

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Dataset Information

Defining and distinguishing infant behavioral states using acoustic cry analysis: is colic painful?

Background

Methods

Results

Conclusions

Publications

Defining and distinguishing infant behavioral states using acoustic cry analysis: is colic painful?

Similar Datasets

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