Investigation of Relation Between Hypoxic-Ischemic Encephalopathy and Spectral Features of Infant Cry Audio

Satar, Mehmet; Cengizler, Caglar; Hamitoglu, Serif; Ozdemir, Mustafa

doi:10.1016/j.jvoice.2022.05.015

Cited by 7 publications

(6 citation statements)

References 19 publications

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“…Twenty-one of the included studies focused on the unintentional air movement across vocal cords by cry, cough, or breath. Machine learning techniques characterized infant cries in the setting of asphyxia [ 34 , 35 , 36 , 37 , 38 ]. Spontaneous pediatric coughs are rigorously described through AI methodology [ 39 , 40 , 41 , 42 , 43 ] and analyzed to detect specific clinical entities such as croup [ 42 , 44 , 45 ], pertussis [ 45 ], asthma [ 46 ], and pneumonia [ 40 ].…”

Section: Discussionmentioning

confidence: 99%

“…Miodonska 2016 [60] Szklanny 2019 [70] Woloshuk 2018 [61] Singapore [2] Balamurali 2021 [52] Hee 2019 [46] South Korea [2] Lee 2020 [19] Lee 2022 [20] Sri Lanka [2] Kariyawasam 2019 [32] Wijesinghe 2019 [27] Sweden [1] Pokorny 2017 [28] Turkey [1] Satar 2022 [38] United Kingdom [1] Alharbi 2018 [51] USA [12] Asgari 2021 [22] Chi 2022 [26] Cho 2019 [17] Ji 2021 [35] Ji 2019 [36] MacFarlane 2022 [23] Manigault 2022 [67] McGinnis 2019 [63] Onu 2019 [37] Sadeghian 2015 [49] Suthar 2022 [50] VanDam 2015 [58] Appendix C…”

Section: Country Study Reference #mentioning

confidence: 99%

“…Amrulloh 2018 [40] Cho 2019 [17] Ji 2021 [35] MacFarlane 2022 [23] McGinnis 2019 [63] Nakai 2017 [24] Pokorny 2022 [25] Tartarisco 2021 [56] Tulics 2018 [57] Short-Time Fourier Transform (STFT) Gouda 2019 [11] Signal-to-Noise Ratio (SNR) Jayasree 2021 [9] Chi 2022 [26] Cho 2019 [17] McGinnis 2019 [63] Nafisah 2019 [69] Satar 2022 [38] Appendix F…”

Section: Appendix Ementioning

confidence: 99%

See 2 more Smart Citations

Voice as a Biomarker of Pediatric Health: A Scoping Review

Rogers,

Hseu,

Kim

et al. 2024

Children

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The human voice has the potential to serve as a valuable biomarker for the early detection, diagnosis, and monitoring of pediatric conditions. This scoping review synthesizes the current knowledge on the application of artificial intelligence (AI) in analyzing pediatric voice as a biomarker for health. The included studies featured voice recordings from pediatric populations aged 0–17 years, utilized feature extraction methods, and analyzed pathological biomarkers using AI models. Data from 62 studies were extracted, encompassing study and participant characteristics, recording sources, feature extraction methods, and AI models. Data from 39 models across 35 studies were evaluated for accuracy, sensitivity, and specificity. The review showed a global representation of pediatric voice studies, with a focus on developmental, respiratory, speech, and language conditions. The most frequently studied conditions were autism spectrum disorder, intellectual disabilities, asphyxia, and asthma. Mel-Frequency Cepstral Coefficients were the most utilized feature extraction method, while Support Vector Machines were the predominant AI model. The analysis of pediatric voice using AI demonstrates promise as a non-invasive, cost-effective biomarker for a broad spectrum of pediatric conditions. Further research is necessary to standardize the feature extraction methods and AI models utilized for the evaluation of pediatric voice as a biomarker for health. Standardization has significant potential to enhance the accuracy and applicability of these tools in clinical settings across a variety of conditions and voice recording types. Further development of this field has enormous potential for the creation of innovative diagnostic tools and interventions for pediatric populations globally.

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Section: Discussionmentioning

confidence: 99%

Section: Country Study Reference #mentioning

confidence: 99%

Section: Appendix Ementioning

confidence: 99%

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Voice as a Biomarker of Pediatric Health: A Scoping Review

Rogers,

Hseu,

Kim

et al. 2024

Children

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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%

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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“…Some researchers have gone more deeply into this topic to diagnose a specific disease. For instance, the authors in [ 42 ] suggested a machine learning model to diagnose hypoxic ischemic encephalopathy disease in newborns based on CAS analysis. Multiple feature extraction techniques were used, including the MFCC and Gammatone frequency cepstral coefficients (GFCCs).…”

Section: Literature Reviewmentioning

confidence: 99%

Infant Cry Signal Diagnostic System Using Deep Learning and Fused Features

2023

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Early diagnosis of medical conditions in infants is crucial for ensuring timely and effective treatment. However, infants are unable to verbalize their symptoms, making it difficult for healthcare professionals to accurately diagnose their conditions. Crying is often the only way for infants to communicate their needs and discomfort. In this paper, we propose a medical diagnostic system for interpreting infants’ cry audio signals (CAS) using a combination of different audio domain features and deep learning (DL) algorithms. The proposed system utilizes a dataset of labeled audio signals from infants with specific pathologies. The dataset includes two infant pathologies with high mortality rates, neonatal respiratory distress syndrome (RDS), sepsis, and crying. The system employed the harmonic ratio (HR) as a prosodic feature, the Gammatone frequency cepstral coefficients (GFCCs) as a cepstral feature, and image-based features through the spectrogram which are extracted using a convolution neural network (CNN) pretrained model and fused with the other features to benefit multiple domains in improving the classification rate and the accuracy of the model. The different combination of the fused features is then fed into multiple machine learning algorithms including random forest (RF), support vector machine (SVM), and deep neural network (DNN) models. The evaluation of the system using the accuracy, precision, recall, F1-score, confusion matrix, and receiver operating characteristic (ROC) curve, showed promising results for the early diagnosis of medical conditions in infants based on the crying signals only, where the system achieved the highest accuracy of 97.50% using the combination of the spectrogram, HR, and GFCC through the deep learning process. The finding demonstrated the importance of fusing different audio features, especially the spectrogram, through the learning process rather than a simple concatenation and the use of deep learning algorithms in extracting sparsely represented features that can be used later on in the classification problem, which improves the separation between different infants’ pathologies. The results outperformed the published benchmark paper by improving the classification problem to be multiclassification (RDS, sepsis, and healthy), investigating a new type of feature, which is the spectrogram, using a new feature fusion technique, which is fusion, through the learning process using the deep learning model.

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Investigation of Relation Between Hypoxic-Ischemic Encephalopathy and Spectral Features of Infant Cry Audio

Cited by 7 publications

References 19 publications

Voice as a Biomarker of Pediatric Health: A Scoping Review

Voice as a Biomarker of Pediatric Health: A Scoping Review

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

Infant Cry Signal Diagnostic System Using Deep Learning and Fused Features

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