Automated detection of perinatal hypoxia using time-frequency-based heart rate variability features.
Perinatal hypoxia is a cause of cerebral injury in foetuses and neonates. Detection of foetal hypoxia during labour based on the pattern recognition of heart rate signals suffers from high observer variability and low specificity. We describe a new automated hypoxia detection method using time-frequ...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 52; no. 2; pp. 183 - 192 |
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| Autores principales: | , , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Feb2014
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104010955&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104010955 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Feb2014 vid: 52 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104010955 NLM24272142 2012455925 10.1007/s11517-013-1129-3 NLM24272142 104010955 ppf: 183 ppct: 9 formats: fmt: @attributes: type: P tig: atl: Automated detection of perinatal hypoxia using time-frequency-based heart rate variability features. aug: au: Dong, Shiying Boashash, Boualem Azemi, Ghasem Lingwood, Barbara E Colditz, Paul B affil: UQ Centre for Clinical Research, The University of Queensland, Herston, QLD, Australia, shiying.dong@uqconnect.edu.au. sug: subj: Anoxia Diagnosis Heart Rate Physiology Signal Processing, Computer Assisted Algorithms Animal Population Groups Animal Studies Electrocardiography Methods Models, Biological Models, Theoretical Monitoring, Physiologic Sensitivity and Specificity Swine Time Factors ab: Perinatal hypoxia is a cause of cerebral injury in foetuses and neonates. Detection of foetal hypoxia during labour based on the pattern recognition of heart rate signals suffers from high observer variability and low specificity. We describe a new automated hypoxia detection method using time-frequency analysis of heart rate variability (HRV) signals. This approach uses features extracted from the instantaneous frequency and instantaneous amplitude of HRV signal components as well as features based on matrix decomposition of the signals' time-frequency distributions using singular value decomposition and non-negative matrix factorization. The classification between hypoxia and non-hypoxia data is performed using a support vector machine classifier. The proposed method is tested on a dataset obtained from a newborn piglet model with a controlled hypoxic insult. The chosen HRV features show strong performance compared to conventional spectral features and other existing methods of hypoxia detection with a sensitivity 93.3 %, specificity 98.3 % and accuracy 95.8 %. The high predictive value of this approach to detecting hypoxia is a substantial step towards developing a more accurate and reliable hypoxia detection method for use in human foetal monitoring. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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