TY - JOUR
T1 - Automated detection of perinatal hypoxia using time-frequency-based heart rate variability features
AU - Dong, Shiying
AU - Boashash, Boualem
AU - Azemi, Ghasem
AU - Lingwood, Barbara E.
AU - Colditz, Paul B.
PY - 2014
Y1 - 2014
N2 - 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.
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.
KW - Foetal hypoxia detection
KW - Heart rate variability
KW - Time–frequency distribution
KW - Instantaneous frequency
KW - Matrix decomposition
UR - http://www.scopus.com/inward/record.url?eid=2-s2.0-84896698178&partnerID=MN8TOARS
U2 - 10.1007/s11517-013-1129-3
DO - 10.1007/s11517-013-1129-3
M3 - Article
C2 - 24272142
SN - 0140-0118
VL - 52
SP - 183
EP - 191
JO - Medical and Biological Engineering and Computing
JF - Medical and Biological Engineering and Computing
ER -