Atypical Cry Acoustics in 6‐Month‐Old Infants at Risk for Autism Spectrum Disorder

Sheinkopf, Stephen J.; Iverson, Jana M.; Rinaldi, Melissa; Lester, Barry M.

doi:10.1002/aur.1244

Cited by 117 publications

(114 citation statements)

References 43 publications

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“…The signs and symptoms of ASD vary widely, especially the child's social communication development which is important for spotting early signs of ASD. Recently, there has been an increasing interest in vocal development of children with ASD and typically developing (TD) children, in particular crying (Esposito, Nakazawa, Venuti, & Bornstein, 2013;Esposito, Venuti, & Bornstein, 2011;Esposito & Venuti, 2009b, 2010bFort & Manfredi, 1998;LaGasse, Neal, & Lester, 2005;Manfredi, Bocchi, Orlandi, Spaccaterra, & Donzelli, 2009;Oller et al, 2010;Orlandi, Manfredi, Bocchi, & Scattoni, 2012;Reyes-Galaviz, Cano-Ortiz, & Reyes-García, 2008;Sheinkopf, Iverson, Rinaldi, & Lester, 2012;Venuti et al, 2012). Several studies reported that children with ASD express atypical patterns of distress vocalization such as higher fundamental frequency (f0),…”

Section: Crying In Typical and Atypical Developmentmentioning

confidence: 99%

“…shorter inter-bout pauses, fewer utterances (Esposito, Nakazawa, Venuti, & Bornstein, 2012;Esposito et al, 2011;Esposito, Nakazawa, et al, 2013;Esposito & Venuti, 2009a, 2010aOller et al, 2010;Sheinkopf et al, 2012). Mostly, the literature in this field focused on crying of infants (such as 1-year old or new-borns).…”

Section: Crying In Typical and Atypical Developmentmentioning

confidence: 99%

“…Mostly, the literature in this field focused on crying of infants (such as 1-year old or new-borns). A recent study examined the acoustic characteristics of cries of 6-month-old infants at high risk of ASD (siblings of children with ASD) and found that they produced cries with higher and more variable f0 than infants not at risk for ASD (Sheinkopf et al, 2012). In addition to atypical patterns of distress vocalization, a number of studies focused on how listeners observe this cry type and showed that adults perceive cries of infants with atypical development differently compared to those of typically developing children (Esposito & Venuti, 2008, 2009aVenuti et al, 2012).…”

Section: Crying In Typical and Atypical Developmentmentioning

confidence: 99%

“…Frequencies outside the 200Hz to 1200Hz were filtered out to remove frequencies that are outside of the cry range and due to noise (Abou-Abbas, Tadj, & Fersaie, 2017;Esposito et al, 2015Esposito et al, , 2011Sheinkopf et al, 2012). The First Spectral Peak (FSP) was obtained by identifying the value of the first amplitude peak of the LTAS output.…”

Section: Acoustic Stimulimentioning

confidence: 99%

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Physiological and self-report responses of parents of children with autism spectrum disorder to children crying

Ozturk

Bizzego

Esposito

et al. 2018

Research in Developmental Disabilities

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Little is known about the physiological response of parents of children with Autism Spectrum Disorder (ASD) to crying of children who have already received the diagnosis of ASD. This study aimed to compare cardiac dynamics via Inter-Beat Interval (IBI) and self-reported emotional states of parents of children with ASD and of parents with typically developing (TD) children while listening to crying of children with ASD (ASD cry) and of typically developing children (TD cry). Analyses revealed higher IBI in parents of children with ASD than IBI in parents of TD children while listening to both cry groups; however no differences on self-reported emotional states were observed. Parents of children with ASD were calmer (higher IBI) than parents of TD children while listening to crying. However, ASD cry did not elicit different IBI compared to TD cry. ASD cry and TD cry were differentiated based on parents' self-responses about what they felt during the listening of crying, their physiological responses showed no differences. These results highlight the similarities and differences between self-reported emotional states and physiological responses of parents of children with ASD, and also point to the importance of monitoring parents' physiological responses in addition to their subjective responses.

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Section: Crying In Typical and Atypical Developmentmentioning

confidence: 99%

Section: Crying In Typical and Atypical Developmentmentioning

confidence: 99%

Section: Crying In Typical and Atypical Developmentmentioning

confidence: 99%

Section: Acoustic Stimulimentioning

confidence: 99%

See 2 more Smart Citations

Physiological and self-report responses of parents of children with autism spectrum disorder to children crying

Ozturk

Bizzego

Esposito

et al. 2018

Research in Developmental Disabilities

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“…Thus, for classification, the same number of 1662 CUs was used for both groups, randomly selected in the TN group. On each CU, the following 22 attributes (parameters) that gained great scientific interest in the last years 31,33,41 were estimated (CU length is given in seconds, values concerning F 0 -F 3 in Hz): CU length, F 0 median, F 0 mean, F 0 standard deviation (F 0 std), F 0 minimum (F 0 min), F 0 maximum (F 0 max), number of estimated F 0 values,…”

Section: Features Extractionmentioning

confidence: 99%

Application of Pattern Recognition Techniques to the Classification of Full-Term and Preterm Infant Cry

et al. 2016

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Summary: Objectives. Scientific and clinical advances in perinatology and neonatology have enhanced the chances of survival of preterm and very low weight neonates. Infant cry analysis is a suitable noninvasive complementary tool to assess the neurologic state of infants particularly important in the case of preterm neonates. This article aims at exploiting differences between full-term and preterm infant cry with robust automatic acoustical analysis and data mining techniques. Study design. Twenty-two acoustical parameters are estimated in more than 3000 cry units from cry recordings of 28 full-term and 10 preterm newborns. Methods. Feature extraction is performed through the BioVoice dedicated software tool, developed at the Biomedical Engineering Lab, University of Firenze, Italy. Classification and pattern recognition is based on genetic algorithms for the selection of the best attributes. Training is performed comparing four classifiers: Logistic Curve, Multilayer Perceptron, Support Vector Machine, and Random Forest and three different testing options: full training set, 10-fold crossvalidation, and 66% split. Results. Results show that the best feature set is made up by 10 parameters capable to assess differences between preterm and full-term newborns with about 87% of accuracy. Best results are obtained with the Random Forest method (receiver operating characteristic area, 0.94). Conclusions. These 10 cry features might convey important additional information to assist the clinical specialist in the diagnosis and follow-up of possible delays or disorders in the neurologic development due to premature birth in this extremely vulnerable population of patients. The proposed approach is a first step toward an automatic infant cry recognition system for fast and proper identification of risk in preterm babies.

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Newborn Cry Acoustics in the Assessment of Neonatal Opioid Withdrawal Syndrome Using Machine Learning

et al. 2022

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ImportanceThe assessment of opioid withdrawal in the neonate, or neonatal opioid withdrawal syndrome (NOWS), is problematic because current assessment methods are based on subjective observer ratings. Crying is a distinctive component of NOWS assessment tools and can be measured objectively using acoustic analysis.ObjectiveTo evaluate the feasibility of using newborn cry acoustics (acoustics referring to the physical properties of sound) as an objective biobehavioral marker of NOWS.Design, Setting, and ParticipantsThis prospective controlled cohort study assessed whether acoustic analysis of neonate cries could predict which infants would receive pharmacological treatment for NOWS. A total of 177 full-term neonates exposed and not exposed to opioids were recruited from Women &amp; Infants Hospital of Rhode Island between August 8, 2016, and March 18, 2020. Cry recordings were processed for 118 neonates, and 65 neonates were included in the final analyses. Neonates exposed to opioids were monitored for signs of NOWS using the Finnegan Neonatal Abstinence Scoring Tool administered every 3 hours as part of a 5-day observation period during which audio was recorded continuously to capture crying. Crying of healthy neonates was recorded before hospital discharge during routine handling (eg, diaper changes).ExposuresThe primary exposure was prenatal opioid exposure as determined by maternal receipt of medication-assisted treatment with methadone or buprenorphine.Main Outcomes and MeasuresNeonates were stratified by prenatal opioid exposure and receipt of pharmacological treatment for NOWS before discharge from the hospital. In total, 775 hours of audio were collected and trimmed into 2.5 hours of usable cries, then acoustically analyzed (using 2 separate acoustic analyzers). Cross-validated supervised machine learning methods (combining the Boruta algorithm and a random forest classifier) were used to identify relevant acoustic parameters and predict pharmacological treatment for NOWS.ResultsFinal analyses included 65 neonates (mean [SD] gestational age at birth, 36.6 [1.1] weeks; 36 [55.4%] female; 50 [76.9%] White) with usable cry recordings. Of those, 19 neonates received pharmacological treatment for NOWS, 7 neonates were exposed to opioids but did not receive pharmacological treatment for NOWS, and 39 healthy neonates were not exposed to opioids. The mean of the predictions of random forest classifiers predicted receipt of pharmacological treatment for NOWS with high diagnostic accuracy (area under the curve, 0.90 [95% CI, 0.83-0.98]; accuracy, 0.85 [95% CI, 0.74-0.92]; sensitivity, 0.89 [95% CI, 0.67-0.99]; specificity, 0.83 [95% CI, 0.69-0.92]).Conclusions and RelevanceIn this study, newborn acoustic cry analysis had potential as an objective measure of opioid withdrawal. These findings suggest that acoustic cry analysis using machine learning could improve the assessment, diagnosis, and management of NOWS and facilitate standardized care for these infants.

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Atypical Cry Acoustics in 6‐Month‐Old Infants at Risk for Autism Spectrum Disorder

Cited by 117 publications

References 43 publications

Physiological and self-report responses of parents of children with autism spectrum disorder to children crying

Physiological and self-report responses of parents of children with autism spectrum disorder to children crying

Application of Pattern Recognition Techniques to the Classification of Full-Term and Preterm Infant Cry

Newborn Cry Acoustics in the Assessment of Neonatal Opioid Withdrawal Syndrome Using Machine Learning

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