Acoustic Cry Characteristics of Infants as a Marker of Neurological Dysfunction: A Systematic Review and Meta-Analysis

Lawford, Harriet; Sazon, Hannah; Richard, C.; Robb, Michael P.; Bora, Samudragupta

doi:10.1016/j.pediatrneurol.2021.10.017

Cited by 5 publications

(7 citation statements)

References 39 publications

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“…Acoustic characteristics of the infant's cry have been associated with prenatal opioid exposure 40,41 and other medical conditions, including asphyxia, hyperbilirubinemia, trisomy anomalies, sudden infant death syndrome, 42 prenatal drug exposure, 41 autism, 43 preterm birth, 44 and other conditions, 45,46 but these studies typically examined a limited subset of acoustic characteristics (most notably, pitch). Pitch is our perception of the fundamental frequency caused by vibration of the vocal folds and has received the most attention because it has intuitive appeal and because variation in pitch can be discriminated by listeners.…”

Section: Discussionmentioning

confidence: 99%

“…40,41,47 It is more likely that multidimensional aspects of cry acoustics have a role in the characterization of NOWS and other conditions, as suggested by a recent meta-analysis of cry characteristics and neurological dysfunction. 45 Of the 17 important variables identified in our analysis, 7 were measures of frequencies in the vocal tract, including the fundamental frequency (pitch). Other frequencies included the first and second formants, which are frequency peaks or resonances as the cry sound travels upward through the vocal tract.…”

Section: Discussionmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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

et al. 2022

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“…Research studies published in the last 20 years focused on the identi cation of the acoustic cry features [5][6][7] to differentiate preterm and full-term newborns 8,9 , neonatal diseases [10][11][12][13][14] , neurodevelopmental disorders 15,16 , and correlation with parents' languages 17,18 through signal processing and arti cial intelligence techniques [19][20][21][22][23] . Main acoustic cry features are characterized by the fundamental frequency (F0), the vibration frequency of the vocal folds 24,25 , the resonance frequencies (F1-F3), resulting from the vocal tract maturation, the parameters concerning vibrato rate and extent and its regularity (jitter and shimmer), and the noise levels 6,19 .…”

Section: Introductionmentioning

confidence: 99%

How can cry acoustics associate newborns’ distress levels with neurophysiological and behavioral signals?

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ACERO³

et al. 2022

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Even though infant crying is a common phenomenon in humans' early life, it is still a challenge for researchers to properly understand it as a reflection of complex neurophysiological functions. Our study aims to determine the association between neonatal cry acoustics with neurophysiological signals and behavioral features according to different distress levels of newborns. Multimodal data from 25 healthy term newborns were collected simultaneously recording infant cry vocalizations, electroencephalography (EEG), near-infrared spectroscopy (NIRS) and videos of facial expressions and body movements. Results showed correlations between most of the features extracted from the signals depending on the infant’s arousal state, among them: fundamental frequency (F0), brain activity (delta, theta and alpha frequency bands), cerebral and body oxygenation, heart rate, facial tension and body rigidity. Additionally, these associations reinforce that what is occurring at an acoustic level can be characterized by behavioral and neurophysiological patterns. Our findings strengthen the potential of crying as a biomarker evidencing the physical, emotional and health status of the infant becoming a crucial tool for caregivers and clinicians.

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“…In the last decade, modern machine learning methods have been tested to classify baby cries. The main research questions were 1. detection of baby cries in different environments, 2. detection of different causes of baby cries (hunger, tiredness, pain, wet diaper), 3. detection of diseases (asphyxia, asthma, deafness, autism, hypothyroidism) with promising results [13, 14]. Machine learning approaches are based on several existing databases of baby cries that were created in different contexts, often with ambient noise, poorly labeled, or not fully accessible.…”

Section: Introductionmentioning

confidence: 99%

Audio Feature Analysis for Acoustic Pain Detection in Term Newborns

et al. 2022

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Introduction: Crying newborns signal a need or discomfort as part of the innate communication system. Exposure to pain is related to infants’ unfavorable neurodevelopmental outcomes. There is a tremendous need for more objective methods to assess neonatal pain. An audio analysis of acoustic utterances could provide specific information on the patient’s pain level. Methods: We analyzed 67 videos of 33 term-born newborns recorded during a planned capillary blood sample, including the stimuli, non-noxious thermal stimulus, short noxious stimulus, and prolonged unpleasant stimulus, between December 2020 and March 2021. Two expert raters evaluated the infants’ pain responses using the Neonatal Facial Coding System (NFCS). The mean values of 123 timbre features of the recorded audio data were analyzed by using specific toolboxes and libraries from the following programming environments: MIRtoolbox (MATLAB), MiningSuite (MATLAB), Essentia (Python), AudioCommons timbral models (Python), and Librosa (Python). Results: The NFCS values were significantly higher during the short noxious stimulus (p < 0.001) and prolonged unpleasant stimulus (p < 0.001) than during the non-noxious thermal stimulus, whereas NFCS values during the short noxious stimulus and prolonged unpleasant stimulus were similar (p = 0.79). Brightness, roughness, percussive energy, and attack times were identified as the features having the highest impact on the NFCS. Conclusion: This hypothesis-generating study identified several salient acoustic features highly associated with pain responses in term newborns. Our analysis is an encouraging starting point for the targeted analysis of pain-specific acoustic features of neonatal cries and vocalizations from the perspective of real-time acoustic processing.

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Acoustic Cry Characteristics of Infants as a Marker of Neurological Dysfunction: A Systematic Review and Meta-Analysis

Cited by 5 publications

References 39 publications

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

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

How can cry acoustics associate newborns’ distress levels with neurophysiological and behavioral signals?

Audio Feature Analysis for Acoustic Pain Detection in Term Newborns

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