QUANTIFYING BABY CRYING RHYTHM ABNORMALITIES USING MULTILAYER PERCEPTRON

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dc.contributor.authorDahmani, Habiba
dc.date.accessioned2018-05-17T09:34:58Z
dc.date.available2018-05-17T09:34:58Z
dc.date.issued2018
dc.description.abstractThere are no studies to quantify rhythm of baby crying signals until now. In the present study, we propose that the introduction of the temporal rhythm metrics as used in the languages identification domain may well characterize the rhythm of newborn infant's sounds. Then, it may improve diagnostic accuracy and helps quickly identify, categorize and discriminate between the sick and healthy babies, therefore, provide more reliability to the evaluation of the health of the newborns. The repeated bursts of expiratory sounds were detected and selected from the crying records of 295 full-term babies of 1 to 90 days old. These recordings occur under some stimulus as pain, fever, diaper change and other different states. The relevance of the proposed rhythm features to distinguish the healthy from the sick babies is approved. The results of various classification experimentations of the correct classification were more than 80%. Indeed, the findings were very promising and confirm the ability of rhythm parameters to characterize the baby crying signal well.en_US
dc.identifier.urihttp://dspace.univ-msila.dz:8080//xmlui/handle/123456789/4377
dc.language.isoenen_US
dc.publisherUniversité de M'silaen_US
dc.subjectSignal Processing, baby crying signal, classification, pathological baby crying signals, rhythm metrics, and neural networksen_US
dc.titleQUANTIFYING BABY CRYING RHYTHM ABNORMALITIES USING MULTILAYER PERCEPTRONen_US
dc.typeArticleen_US

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