دورية أكاديمية
Extraction of Premature Newborns' Spontaneous Cries in the Real Context of Neonatal Intensive Care Units
| العنوان: | Extraction of Premature Newborns' Spontaneous Cries in the Real Context of Neonatal Intensive Care Units |
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| المؤلفون: | Cabon, Sandie, Met-Montot, Bertille, Poree, Fabienne, Rosec, Olivier, Simon, Antoine, Carrault, Guy |
| المساهمون: | Laboratoire Traitement du Signal et de l'Image (LTSI), Université de Rennes (UR)-Institut National de la Santé et de la Recherche Médicale (INSERM), Voxygen Pleumeur-Bodou, Voxygen, European Union European Commission 689260, European Project: 689260,H2020,H2020-PHC-2015-single-stage,Digi-NewB(2016) |
| المصدر: | ISSN: 1424-8220 ; Sensors ; https://hal.science/hal-03629507Test ; Sensors, 2022, 22 (5), pp.1823. ⟨10.3390/s22051823⟩. |
| بيانات النشر: | HAL CCSD MDPI |
| سنة النشر: | 2022 |
| المجموعة: | Université de Rennes 1: Publications scientifiques (HAL) |
| مصطلحات موضوعية: | audio processing, spontaneous cry extraction, harmonic plus noise analysis, classification, real context, NICU, continuous monitoring, preterms, neuro-behavioral development, [SDV.IB]Life Sciences [q-bio]/Bioengineering |
| الوصف: | International audience ; Cry analysis is an important tool to evaluate the development of preterm infants. However, the context of Neonatal Intensive Care Units is challenging, since a wide variety of sounds can occur (e.g., alarms and adult voices). In this paper, a method to extract cries is proposed. It is based on an initial segmentation between silence and sound events, followed by feature extraction on the resulting audio segments and a cry and non-cry classification. A database of 198 cry events coming from 21 newborns and 439 non-cry events was created. Then, a set of features-including Mel-Frequency Cepstral Coefficients-issued from principal component analysis, was computed to describe each audio segment. For the first time in cry analysis, noise was handled using harmonic plus noise analysis. Several machine learning models have been compared. The K-Nearest Neighbours approach showed the best results with a precision of 92.9%. To test the approach in a monitoring application, 412 h of recordings were automatically processed. The cries automatically selected were replayed and a precision of 92.2% was obtained. The impact of errors on the fundamental frequency characterisation was also studied. Results show that despite a difficult context, automatic cry extraction for non-invasive monitoring of vocal development of preterm infants is achievable. |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | English |
| العلاقة: | info:eu-repo/semantics/altIdentifier/pmid/35270967; info:eu-repo/grantAgreement//689260/EU/Non-invasive monitoring of perinatal health through multiparametric digital representation of clinically relevant functions for improving clinical intervention in neonatal units (Digi-NewB)/Digi-NewB; hal-03629507; https://hal.science/hal-03629507Test; https://hal.science/hal-03629507/documentTest; https://hal.science/hal-03629507/file/sensors-22-01823.pdfTest; PUBMED: 35270967; PUBMEDCENTRAL: PMC8915127 |
| DOI: | 10.3390/s22051823 |
| الإتاحة: | https://doi.org/10.3390/s22051823Test https://hal.science/hal-03629507Test https://hal.science/hal-03629507/documentTest https://hal.science/hal-03629507/file/sensors-22-01823.pdfTest |
| حقوق: | http://creativecommons.org/licenses/byTest/ ; info:eu-repo/semantics/OpenAccess |
| رقم الانضمام: | edsbas.C5755B45 |
| قاعدة البيانات: | BASE |
| DOI: | 10.3390/s22051823 |
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