Med Biol Eng Comput - Automated detection of perinatal hypoxia using time-frequency-based heart rate variability features.

Tópicos

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Resumo

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.

Resumo Limpo

perinat hypoxia caus cerebr injuri foetus neonat detect foetal hypoxia labour base pattern recognit heart rate signal suffer high observ variabl low specif describ new autom hypoxia detect method use timefrequ analysi heart rate variabl hrv signal approach use featur extract instantan frequenc instantan amplitud hrv signal compon well featur base matrix decomposit signal timefrequ distribut use singular valu decomposit nonneg matrix factor classif hypoxia nonhypoxia data perform use support vector machin classifi propos method test dataset obtain newborn piglet model control hypox insult chosen hrv featur show strong perform compar convent spectral featur exist method hypoxia detect sensit specif accuraci high predict valu approach detect hypoxia substanti step toward develop accur reliabl hypoxia detect method use human foetal monitor

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