Identification of Diseases in Newborns Using Advanced Acoustic Features of Cry Signals.
Identifieur interne : 000252 ( Main/Exploration ); précédent : 000251; suivant : 000253Identification of Diseases in Newborns Using Advanced Acoustic Features of Cry Signals.
Auteurs : Yasmina Kheddache [Algérie] ; Chakib Tadj [Canada]Source :
- Biomedical signal processing and control [ 1746-8094 ] ; 2019.
Abstract
Our challenge in the current study is to extend research on the cries of newborns for the early diagnosis of different pathologies. This paper proposes a recognition system for healthy and pathological cries using a probabilistic neural network classifier. Two different kinds of features have been used to characterize newborn cry signals: 1) acoustic features such as fundamental frequency glide (F0glide) and resonance frequencies dysregulation (RFsdys); 2) conventional features such as mel-frequency cestrum coefficients. This paper describes the automatic estimation of the proposed characteristics and the performance evaluation of these features in identifying pathological cries. The adopted methods for F0glides and RFsdys estimation are based on the derived function of the F0 contour and the jump "J" of the RFs between two subsequent tunings, respectively. The database used contains 3250 cry samples of full-term and preterm newborns, and includes healthy and pathologic cries. The obtained results indicate the important association between the quantified features and some studied pathologies, and also an improvement in the identification of pathologic cries. The best result obtained is 88.71% for the correct identification of health status of preterm newborns, and 82% for the correct identification of full-term infants with a specific disease. We conclude that using the proposed characteristics improves the diagnosis of pathologies in newborns. Moreover, the method applied in the estimation of these characteristics allows us to extend this study to other uninvestigated pathologies.
DOI: 10.1016/j.bspc.2019.01.010
PubMed: 33281921
PubMed Central: PMC7672377
Affiliations:
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This paper proposes a recognition system for healthy and pathological cries using a probabilistic neural network classifier. Two different kinds of features have been used to characterize newborn cry signals: 1) acoustic features such as fundamental frequency glide (F<sub>0glide</sub>) and resonance frequencies dysregulation (RFs<sub>dys</sub>); 2) conventional features such as mel-frequency cestrum coefficients. This paper describes the automatic estimation of the proposed characteristics and the performance evaluation of these features in identifying pathological cries. The adopted methods for F<sub>0glides</sub> and RFs<sub>dys</sub> estimation are based on the derived function of the F0 contour and the jump "J" of the RFs between two subsequent tunings, respectively. The database used contains 3250 cry samples of full-term and preterm newborns, and includes healthy and pathologic cries. The obtained results indicate the important association between the quantified features and some studied pathologies, and also an improvement in the identification of pathologic cries. The best result obtained is 88.71% for the correct identification of health status of preterm newborns, and 82% for the correct identification of full-term infants with a specific disease. We conclude that using the proposed characteristics improves the diagnosis of pathologies in newborns. Moreover, the method applied in the estimation of these characteristics allows us to extend this study to other uninvestigated pathologies.</div></front></TEI><pubmed><MedlineCitation Status="PubMed-not-MEDLINE" Owner="NLM"><PMID Version="1">33281921</PMID><DateRevised><Year>2020</Year><Month>12</Month><Day>08</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Print">1746-8094</ISSN><JournalIssue CitedMedium="Print"><Volume>50</Volume><PubDate><Year>2019</Year></PubDate></JournalIssue><Title>Biomedical signal processing and control</Title><ISOAbbreviation>Biomed Signal Process Control</ISOAbbreviation></Journal><ArticleTitle>Identification of Diseases in Newborns Using Advanced Acoustic Features of Cry Signals.</ArticleTitle><Pagination><MedlinePgn>35-44</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1016/j.bspc.2019.01.010</ELocationID><Abstract><AbstractText>Our challenge in the current study is to extend research on the cries of newborns for the early diagnosis of different pathologies. This paper proposes a recognition system for healthy and pathological cries using a probabilistic neural network classifier. Two different kinds of features have been used to characterize newborn cry signals: 1) acoustic features such as fundamental frequency glide (F<sub>0glide</sub>) and resonance frequencies dysregulation (RFs<sub>dys</sub>); 2) conventional features such as mel-frequency cestrum coefficients. This paper describes the automatic estimation of the proposed characteristics and the performance evaluation of these features in identifying pathological cries. The adopted methods for F<sub>0glides</sub> and RFs<sub>dys</sub> estimation are based on the derived function of the F0 contour and the jump "J" of the RFs between two subsequent tunings, respectively. The database used contains 3250 cry samples of full-term and preterm newborns, and includes healthy and pathologic cries. The obtained results indicate the important association between the quantified features and some studied pathologies, and also an improvement in the identification of pathologic cries. The best result obtained is 88.71% for the correct identification of health status of preterm newborns, and 82% for the correct identification of full-term infants with a specific disease. We conclude that using the proposed characteristics improves the diagnosis of pathologies in newborns. Moreover, the method applied in the estimation of these characteristics allows us to extend this study to other uninvestigated pathologies.</AbstractText><CopyrightInformation>© 2019 The Author(s).</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Kheddache</LastName><ForeName>Yasmina</ForeName><Initials>Y</Initials><AffiliationInfo><Affiliation>Faculty of Science and Technology, Ziane Achour University, 3117 Djelfa, Algeria.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Tadj</LastName><ForeName>Chakib</ForeName><Initials>C</Initials><AffiliationInfo><Affiliation>Department of Electrical Engineering, École de technologie supérieure, H3C 1K3 Montréal (Qc), Canada.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2019</Year><Month>04</Month><Day>01</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>Biomed Signal Process Control</MedlineTA><NlmUniqueID>101317299</NlmUniqueID><ISSNLinking>1746-8094</ISSNLinking></MedlineJournalInfo><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">Classification</Keyword><Keyword MajorTopicYN="N">F0 glides</Keyword><Keyword MajorTopicYN="N">Mel-frequency cestrum coefficients</Keyword><Keyword MajorTopicYN="N">Pathologic cry</Keyword><Keyword MajorTopicYN="N">Probabilistic neural network</Keyword><Keyword MajorTopicYN="N">RF dysregulations</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="entrez"><Year>2020</Year><Month>12</Month><Day>7</Day><Hour>5</Hour><Minute>32</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2019</Year><Month>1</Month><Day>1</Day><Hour>0</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2019</Year><Month>1</Month><Day>1</Day><Hour>0</Hour><Minute>1</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">33281921</ArticleId><ArticleId IdType="doi">10.1016/j.bspc.2019.01.010</ArticleId><ArticleId IdType="pii">BSPC-50-035</ArticleId><ArticleId IdType="pmc">PMC7672377</ArticleId></ArticleIdList><pmc-dir>pmcsd</pmc-dir>
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