Identification of Diseases in Newborns Using Advanced Acoustic Features of Cry Signals
Identifieur interne : 000116 ( Pmc/Checkpoint ); précédent : 000115; suivant : 000117Identification 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.
Url:
DOI: 10.1016/j.bspc.2019.01.010
PubMed: 33281921
PubMed Central: 7672377
Affiliations:
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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Identification of Diseases in Newborns Using Advanced Acoustic Features of Cry Signals</title><author><name sortKey="Kheddache, Yasmina" sort="Kheddache, Yasmina" uniqKey="Kheddache Y" first="Yasmina" last="Kheddache">Yasmina Kheddache</name><affiliation wicri:level="1"><nlm:aff id="aff0001">Faculty of Science and Technology, Ziane Achour University, 3117 Djelfa, Algeria</nlm:aff><country xml:lang="fr">Algérie</country><wicri:regionArea>Faculty of Science and Technology, Ziane Achour University, 3117 Djelfa</wicri:regionArea></affiliation></author><author><name sortKey="Tadj, Chakib" sort="Tadj, Chakib" uniqKey="Tadj C" first="Chakib" last="Tadj">Chakib Tadj</name><affiliation wicri:level="1"><nlm:aff id="aff0002">Department of Electrical Engineering, École de technologie supérieure, H3C 1K3 Montréal (Qc), Canada</nlm:aff><country xml:lang="fr">Canada</country><wicri:regionArea>Department of Electrical Engineering, École de technologie supérieure, H3C 1K3 Montréal (Qc)</wicri:regionArea></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">33281921</idno><idno type="pmc">7672377</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7672377</idno><idno type="RBID">PMC:7672377</idno><idno type="doi">10.1016/j.bspc.2019.01.010</idno><date when="2019">2019</date><idno type="wicri:Area/Pmc/Corpus">000167</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">000167</idno><idno type="wicri:Area/Pmc/Curation">000167</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Curation">000167</idno><idno type="wicri:Area/Pmc/Checkpoint">000116</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Checkpoint">000116</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Identification of Diseases in Newborns Using Advanced Acoustic Features of Cry Signals</title><author><name sortKey="Kheddache, Yasmina" sort="Kheddache, Yasmina" uniqKey="Kheddache Y" first="Yasmina" last="Kheddache">Yasmina Kheddache</name><affiliation wicri:level="1"><nlm:aff id="aff0001">Faculty of Science and Technology, Ziane Achour University, 3117 Djelfa, Algeria</nlm:aff><country xml:lang="fr">Algérie</country><wicri:regionArea>Faculty of Science and Technology, Ziane Achour University, 3117 Djelfa</wicri:regionArea></affiliation></author><author><name sortKey="Tadj, Chakib" sort="Tadj, Chakib" uniqKey="Tadj C" first="Chakib" last="Tadj">Chakib Tadj</name><affiliation wicri:level="1"><nlm:aff id="aff0002">Department of Electrical Engineering, École de technologie supérieure, H3C 1K3 Montréal (Qc), Canada</nlm:aff><country xml:lang="fr">Canada</country><wicri:regionArea>Department of Electrical Engineering, École de technologie supérieure, H3C 1K3 Montréal (Qc)</wicri:regionArea></affiliation></author></analytic><series><title level="j">Biomedical Signal Processing and Control</title><idno type="ISSN">1746-8094</idno><imprint><date when="2019">2019</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>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.</p><p>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.</p><p>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.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Fausto, O" uniqKey="Fausto O">O. Fausto</name></author><author><name sortKey="Galaviz, R" uniqKey="Galaviz R">R. 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<front><journal-meta><journal-id journal-id-type="nlm-ta">Biomed Signal Process Control</journal-id><journal-id journal-id-type="iso-abbrev">Biomed Signal Process Control</journal-id><journal-id journal-id-type="publisher-id">BSPC</journal-id><journal-title-group><journal-title>Biomedical Signal Processing and Control</journal-title></journal-title-group><issn pub-type="ppub">1746-8094</issn><publisher><publisher-name>Elsevier Ltd.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">33281921</article-id><article-id pub-id-type="pmc">7672377</article-id><article-id pub-id-type="publisher-id">BSPC-50-035</article-id><article-id pub-id-type="doi">10.1016/j.bspc.2019.01.010</article-id><article-categories><subj-group subj-group-type="heading"><subject>Articles</subject></subj-group></article-categories><title-group><article-title>Identification of Diseases in Newborns Using Advanced Acoustic Features of Cry Signals</article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Kheddache</surname><given-names>Yasmina</given-names></name><xref ref-type="aff" rid="aff0001">*</xref><xref ref-type="corresp" rid="cor1"></xref></contrib><contrib contrib-type="author"><name><surname>Tadj</surname><given-names>Chakib</given-names></name><xref ref-type="aff" rid="aff0002">**</xref></contrib><aff id="aff0001"><label>*</label>Faculty of Science and Technology, Ziane Achour University, 3117 Djelfa, Algeria</aff><aff id="aff0002"><label>**</label>Department of Electrical Engineering, École de technologie supérieure, H3C 1K3 Montréal (Qc), Canada</aff></contrib-group><author-notes><corresp id="cor1"><bold><italic>Corresponding Author:</italic></bold> Yasmina Kheddache, e-mail: <email xlink:href="yasmina.kheddache.1@etsmtl.net">yasmina.kheddache.1@etsmtl.net</email> fax: + (001) 514 396-8684</corresp></author-notes><pub-date pub-type="epub"><day>01</day><month>4</month><year>2019</year></pub-date><pub-date pub-type="ppub"><year>2019</year></pub-date><volume>50</volume><fpage>35</fpage><lpage>44</lpage><permissions><copyright-statement>© 2019 The Author(s).</copyright-statement><copyright-year>2019</copyright-year><license license-type="open-access" xlink:href="http://creativecommons.org/licenses/by-nc-nd/4.0/"><license-p>This manuscript version is made available under the CC-BY-NC-ND 4.0 license.</license-p></license></permissions><abstract><p>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.</p><p>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.</p><p>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.</p></abstract><kwd-group><kwd>Pathologic cry</kwd><kwd>Classification</kwd><kwd>Probabilistic neural network</kwd><kwd>Mel-frequency cestrum coefficients</kwd><kwd>RF dysregulations</kwd><kwd>F<sub>0</sub> glides</kwd></kwd-group></article-meta></front></pmc><affiliations><list><country><li>Algérie</li><li>Canada</li></country></list><tree><country name="Algérie"><noRegion><name sortKey="Kheddache, Yasmina" sort="Kheddache, Yasmina" uniqKey="Kheddache Y" first="Yasmina" last="Kheddache">Yasmina Kheddache</name></noRegion></country><country name="Canada"><noRegion><name sortKey="Tadj, Chakib" sort="Tadj, Chakib" uniqKey="Tadj C" first="Chakib" last="Tadj">Chakib Tadj</name></noRegion></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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