Improved binary dragonfly optimization algorithm and wavelet packet based non-linear features for infant cry classification

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dc.authorid0000-0003-1760-2473en_US
dc.authorid0000-0002-8929-3473en_US
dc.authorid0000-0002-3527-8825en_US
dc.authorid0000-0003-1840-9958en_US
dc.contributor.authorHariharan, Muthusamy
dc.contributor.authorSindhu, Ru
dc.contributor.authorVijean, Vikneswaran
dc.contributor.authorYazid, Haniza
dc.contributor.authorNadarajaw, Thiyagar
dc.contributor.authorYaacob, Sazali
dc.contributor.authorPolat, Kemal
dc.date.accessioned2021-06-23T19:49:56Z
dc.date.available2021-06-23T19:49:56Z
dc.date.issued2018
dc.departmentBAİBÜ, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.description.abstractBackground and objective: Infant cry signal carries several levels of information about the reason for crying (hunger, pain, sleepiness and discomfort) or the pathological status (asphyxia, deaf, jaundice, premature condition and autism, etc.) of an infant and therefore suited for early diagnosis. In this work, combination of wavelet packet based features and Improved Binary Dragonfly Optimization based feature selection method was proposed to classify the different types of infant cry signals. Methods: Cry signals from 2 different databases were utilized. First database contains 507 cry samples of normal (N), 340 cry samples of asphyxia (A), 879 cry samples of deaf (D), 350 cry samples of hungry (H) and 192 cry samples of pain (P). Second database contains 513 cry samples of jaundice (J), 531 samples of premature (Prem) and 45 samples of normal (N). Wavelet packet transform based energy and non-linear entropies (496 features), Linear Predictive Coding (LPC) based cepstral features (56 features), Mel-frequency Cepstral Coefficients (MFCCs) were extracted (16 features). The combined feature set consists of 568 features. To overcome the curse of dimensionality issue, improved binary dragonfly optimization algorithm (IBDFO) was proposed to select the most salient attributes or features. Finally, Extreme Learning Machine (ELM) kernel classifier was used to classify the different types of infant cry signals using all the features and highly informative features as well. Results: Several experiments of two-class and multi-class classification of cry signals were conducted. In binary or two-class experiments, maximum accuracy of 90.18% for H Vs P, 100% for A Vs N, 100% for D Vs N and 97.61% J Vs Prem was achieved using the features selected (only 204 features out of 568) by IBDFO. For the classification of multiple cry signals (multi-class problem), the selected features could differentiate between three classes (N, A & D) with the accuracy of 100% and seven classes with the accuracy of 97.62%. Conclusion: The experimental results indicated that the proposed combination of feature extraction and selection method offers suitable classification accuracy and may be employed to detect the subtle changes in the cry signals. (c) 2017 Elsevier B.V. All rights reserved.en_US
dc.identifier.doi10.1016/j.cmpb.2017.11.021
dc.identifier.endpage51en_US
dc.identifier.issn0169-2607
dc.identifier.issn1872-7565
dc.identifier.pmid29512503en_US
dc.identifier.scopus2-s2.0-85036611215en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage39en_US
dc.identifier.urihttps://doi.org/10.1016/j.cmpb.2017.11.021
dc.identifier.urihttps://hdl.handle.net/20.500.12491/9656
dc.identifier.volume155en_US
dc.identifier.wosWOS:000424763400006en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.institutionauthorPolat, Kemal
dc.language.isoenen_US
dc.publisherElsevier Ireland Ltden_US
dc.relation.ispartofComputer Methods And Programs In Biomedicineen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectInfant Cry Signalen_US
dc.subjectFeature Extractionen_US
dc.subjectFeature Selectionen_US
dc.subjectOptimization and Classificationen_US
dc.titleImproved binary dragonfly optimization algorithm and wavelet packet based non-linear features for infant cry classificationen_US
dc.typeArticleen_US

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