Investigation of Relation Between Hypoxic-Ischemic Encephalopathy and Spectral Features of Infant Cry Audio


      Despite advances in medical technologies, Hypoxic-Ischemic Encephalopathy (HIE) continues to be a problem for neonatal intensive care units. Analysis of crying sounds may be a valuable tool for predicting neonatal disease. However, the characteristics of crying in newborns with HIE are still unclear. One of the factors limiting the ability to focus on that subject is the lack of commercially available infant cry database for research. Also, another reason that complicates the classification is the varying characteristics of infant cry. Accordingly, crying sounds were recorded from 35 infants and demographic characteristics of the study groups are presented as well as the numerical representation of spectral features. Experiments reveal that the existence of HIE causes distinctive variation in energy, energy entropy and spectral centroid features of the utterances; which leads us to conclude that the presented combination of spectral features would function well with any supervised or unsupervised machine learning algorithm.

      Key Words

      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'


      Subscribe to Journal of Voice
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect


        • Parish A.
        • Bhatia J.
        Hypothermia for hypoxic–ischemic brain injury.
        J Maternal-Fetal Neonatal Med. 2009; 22: 719-721
        • Pin T.W.
        • Eldridge B.
        • Galea M.P.
        A review of developmental outcomes of term infants with post-asphyxia neonatal encephalopathy.
        Eur J Paediatr Neurol. 2009; 13: 224-234
        • Lundgren C.
        • Brudin L.
        • Wanby A.-S.
        • et al.
        Ante-and intrapartum risk factors for neonatal hypoxic ischemic encephalopathy.
        J Maternal-Fetal Neonatal Med. 2018; 31: 1595-1601
        • Martinello K.
        • Hart A.R.
        • Yap S.
        • et al.
        Management and investigation of neonatal encephalopathy: 2017 update.
        Arch Dis Childhood-Fetal Neonatal Ed. 2017; 102: F346-F358
        • Lee A.C.
        • Kozuki N.
        • Blencowe H.
        • et al.
        Intrapartum-related neonatal encephalopathy incidence and impairment at regional and global levels for 2010 with trends from 1990.
        Pediatr Res. 2013; 74: 50-72
        • Türk Neonatoloji Derneği Hipoksik İskemik Ensefalopati Çalışma Grubu
        Türkiye’de yenidoğan yoğun bakım ünitelerinde izlenen hipoksik iskemik ensefalopatili olgular, risk faktörleri, insidans ve kısa dönem prognozları.
        Çocuk Sağlığı ve Hastalıkları Dergisi. 2008; 51: 123-129
        • Fort A.
        • Manfredi C.
        Acoustic analysis of newborn infant cry signals.
        Medical engineering & physics. 1998; 20: 432-442
        • Robb M.P.
        • Crowell D.H.
        • Dunn-Rankin P.
        Cry analysis in infants resuscitated for apnea of infancy.
        Int J Pediatr Otorhinolaryngol. 2007; 71: 1117-1123
        • Bellieni C.V.
        • Sisto R.
        • Cordelli D.M.
        • et al.
        Cry features reflect pain intensity in term newborns: an alarm threshold.
        Pediatr Res. 2004; 55: 142-146
        • Chittora A.
        • Patil H.A.
        Data collection of infant cries for research and analysis.
        J Voice. 2017; 31: 252-e15 2016.07.007
        • Kheddache Y.
        • Tadj C.
        Identification of diseases in newborns using advanced acoustic features of cry signals.
        Biomed Signal Process Control. 2019; 50: 35-44
        • Satar M.
        • Cengizler C.
        • Hamitoglu S.
        • et al.
        Audio analysis based diagnosis of hypoxic ischemic encephalopathy in newborns.
        Int J Adv Biomed Eng. 2022; 1: 28-42
        • Thompson C.
        • Puterman A.
        • Linley L.
        • et al.
        The value of a scoring system for hypoxic ischaemic encephalopathy in predicting neurodevelopmental outcome.
        Acta Paediatrica. 1997; 86: 757-761
        • Martinello K.
        • Hart A.R.
        • Yap S.
        • et al.
        Management and investigation of neonatal encephalopathy: 2017 update.
        Arch Dis Childhood-Fetal Neonatal Ed. 2017; 102: F346-F358
        • Kheddache Y.
        • Tadj C.
        Resonance frequencies behavior in pathologic cries of newborns.
        J Voice. 2015; 29: 1-12
        • Alaie H.F.
        • Abou-Abbas L.
        • Tadj C.
        Cry-based infant pathology classification using gmms.
        Speech Commun. 2016; 77: 28-52
        • Aresta M.
        • Dibenedetto A.
        • Quaranta E.
        State of the art and perspectives in catalytic processes for co2 conversion into chemicals and fuels: the distinctive contribution of chemical catalysis and biotechnology.
        J Catal. 2016; 343: 2-45
        • Quast A.
        • Hesse V.
        • Hain J.
        • et al.
        Baby babbling at five months linked to sex hormone levels in early infancy.
        Infant Behav Dev. 2016; 44: 1-10
        • Wermke K.
        • Cebulla M.
        • Salinger V.
        • et al.
        Cry features of healthy neonates who passed their newborn hearing screening vs. those who did not.
        Int J Pediatr Otorhinolaryngol. 2021; 144: 110689
        • Peeters G.
        A large set of audio features for sound description (similarity and classification) in the cuidado project.
        CUIDADO Ist Project Rep. 2004; 54: 1-25
        • Vakkuri A.
        • Yli-Hankala A.
        • Talja P.
        • et al.
        Time-frequency balanced spectral entropy as a measure of anesthetic drug effect in central nervous system during sevoflurane, propofol, and thiopental anesthesia.
        Acta Anaesthesiologica Scandinavica. 2004; 48: 145-153
        • Hughes M.S.
        A comparison of shannon entropy versus signal energy for acoustic detection of artificially induced defects in plexiglas.
        J Acoust Soc Am. 1992; 91: 2272-2275
        • Scheirer E.
        • Slaney M.
        Construction and evaluation of a robust multifeature speech/music discriminator.
        1997 IEEE international conference on acoustics, speech, and signal processing. vol. 2. IEEE, 1997: 1331-1334
        • Zhang Y.
        • Liu B.
        • Shi Q.
        Energy entropy feature and diagnosis of partial discharge wavelet packet in gis based on support vector machine.
        2020 12th IEEE PES Asia-Pacific power and energy engineering conference (APPEEC). IEEE, 2020: 1-5
      1. Bachu R, Kopparthi S, Adapa B, et al. Separation of voiced and unvoiced using zero crossing rate and energy of the speech signal. American Society for Engineering Education (ASEE) zone conference proceedings. 2008:1–7.

        • Kumari M.
        • Kumar P.
        • Solanki S.
        Classification of north indian musical instruments using spectral features.
        Comput Sci Telecommun. 2010; 29: 11-24
        • Arbelaitz O.
        • Gurrutxaga I.
        • Muguerza J.
        • et al.
        An extensive comparative study of cluster validity indices.
        Pattern Recognit. 2013; 46: 243-256
        • Na S.
        • Xumin L.
        • Yong G.
        Research on k-means clustering algorithm: an improved k-means clustering algorithm.
        2010 third international symposium on intelligent information technology and security informatics. IEEE, 2010: 63-67
        • Santos J.M.
        • Embrechts M.
        On the use of the adjusted rand index as a metric for evaluating supervised classification.
        International conference on artificial neural networks. Springer, 2009: 175-184
      2. Easterbrooks M, Bartlett JD, Beeghly M, et al. Social and emotional development in infancy. 2013:91–120.

        • Dewi S.P.
        • Prasasti A.L.
        • Irawan B.
        The study of baby crying analysis using mfcc and lfcc in different classification methods.
        2019 IEEE international conference on signals and systems (ICSigSys). IEEE, 2019: 18-23
        • Sailor H.B.
        • Patil H.A.
        2. unsupervised auditory filterbank learning for infant cry classification.
        Acoustic analysis of pathologies. De Gruyter, 2020: 63-92
        • Chittora A.
        • Patil H.A.
        Classification of pathological infant cries using modulation spectrogram features.
        The 9th International Symposium on Chinese Spoken Language Processing. IEEE, 2014: 541-545
        • Chittora A.
        • Patil H.A.
        Modified group delay based features for asthma and hie infant cries classification.
        International Conference on Text, Speech, and Dialogue. Springer, 2015: 595-602
        • Rajoub B.
        Characterization of biomedical signals: feature engineering and extraction.
        Biomedical signal processing and artificial intelligence in healthcare. Elsevier, 2020: 29-50